WEARABLE ACTIVITY MONITORS ON CRIME SCENE … · positioning. Smartwatches are one such modern wearable device and are expected to make up 60% of the forecast $25 billion wearables

WEARABLE ACTIVITY MONITORS ON CRIME SCENE INVESTIGATORS FOR MONITORING

GEOLOCATION AND PHYSIOLOGICAL INDICATORS OF STRESS AND FATIGUE

By

Elizabeth Morahan

A thesis submitted in fulfilment of the requirements for the degree of

Master of Forensic Science (Professional Practice)

in

The School of Veterinary and Life Sciences

Murdoch University

Supervisor: Mr Brendan Chapman

Semester 1, 2018

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Declaration

I declare that this thesis does not contain any material submitted previously for the award of any other

degree or diploma at any university or other tertiary institution. Furthermore, to the best of my

knowledge, it does not contain any material previously published or written by another individual, except

where due reference has been made in the text. Finally, I declare that all reported experimentations

performed in this research were carried out by myself, except that any contribution by others, with whom I

have worked is explicitly acknowledged.

Signed: Elizabeth Morahan (13th of July 2018)

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Acknowledgements

The author wishes to sincerely thank the following people for their assistance in the completion of this thesis:

To my supervisor, Brendan Chapman, for his tireless support throughout the research. I have never encountered

such dedication to students as you have provided us, and I appreciate it greatly. You honestly went above and

beyond, and I cannot thank you enough. I couldn’t have completed this without you!

To Mr Giles Oatley, for his assistance with all things QGis. Thank you especially for your help with the heatmaps, your

expertise

Lastly, a very special thank you to all the people who have supported me on my way, especially my parents and

sisters, for all the reassurance, positivity and countless cups of tea; and my fellow Masters peers, Nick Booth, Beau

Shankland, Justin Sim and Lena Tran, for their solidarity, encouragement and advice.

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1 TABLE OF CONTENTS

Title Page ............................................................................................................................................. i

Declaration ......................................................................................................................................... ii

Acknowledgements ........................................................................................................................... iii

Part One

Literature Review ........................................................................................................................... 3-47

Part Two

Manuscript ................................................................................................................................... 49-95

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This page has been left intentionally blank

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Part 1:

WEARABLE ACTIVITY MONITORS ON CRIME SCENE INVESTIGATORS FOR MONITORING

GEOLOCATION AND PHYSIOLOGICAL INDICATORS OF STRESS AND FATIGUE

Literature Review

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TABLE OF CONTENTS

2 List of Figures ............................................................................................................. Error! Bookmark not defined.

3 Abstract ..................................................................................................................................................................... 5

4 Literature Review ...................................................................................................................................................... 6

4.1 Introduction ...................................................................................................................................................... 6

4.2 Wearable devices .............................................................................................................................................. 8

4.3 Fatigue ............................................................................................................................................................... 9

4.3.1 Mechanism .............................................................................................................................................. 10

4.3.2 Fatigue and crime scene investigation .................................................................................................... 11

4.3.3 Monitoring fatigue in the workplace using wearables ........................................................................... 12

4.4 Stress ............................................................................................................................................................... 13

4.4.1 Mechanism .............................................................................................................................................. 13

4.4.2 Stress in Crime Scene Investigation ........................................................................................................ 14

4.4.3 Monitoring stress in crime scenes using wearables ............................................................................... 15

4.5 Gauging Stress and Fatigue in Individuals ....................................................................................................... 16

4.5.1 Blood Pressure ........................................................................................................................................ 17

4.5.2 Heart Rate ............................................................................................................................................... 19

4.5.3 Body Temperature .................................................................................................................................. 21

4.5.4 Saturated O2 Levels ................................................................................................................................. 22

4.6 Location Tracking of Individuals and Objects.................................................................................................. 23

4.6.1 RFID Tracking ........................................................................................................................................... 23

4.6.2 Global Navigation Satellite Systems ........................................................................................................ 25

4.6.3 GPS .......................................................................................................................................................... 26

4.6.4 GLONASS ................................................................................................................................................. 27

4.6.5 The accuracy of GPS and GLONASS ......................................................................................................... 27

4.6.6 Current GNSS Applications ...................................................................................................................... 29

4.6.7 Geodetic Datum ...................................................................................................................................... 30

4.7 Aims of the Proposed Study ............................................................................................................................ 31

4.8 Hypotheses...................................................................................................................................................... 31

4.8.1 Hypothesis 1 ............................................................................................................................................ 31

4.8.2 Hypothesis 2 ............................................................................................................................................ 32

4.9 PROPOSED METHODOLOGY............................................................................................................................ 32

4.9.1 Monitoring Physiological indicators of stress and fatigue ...................................................................... 33

4.9.2 Geotracking Investigators ....................................................................................................................... 33

5 Conclusion ............................................................................................................................................................... 34

6 References .............................................................................................................................................................. 36

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2 ABSTRACT

Modern consumers have embraced wearable activity monitors, with sales expected to total 340 million units this

year, worldwide. Devices can track a user’s location, as well as physiological measures such as heart rate. These

functions have been used in various settings, including in elite sports, workplaces and clinical environments, and also

have the potential to be applied to crime scene investigation in two ways. Firstly, by utilising satellite positioning

functions to monitor the geolocation of personnel, the need for a crime scene entry/exit log may be made

redundant as an individual is geotracked throughout the crime scene as the investigation progresses. Secondly, by

monitoring physiological metrics which are indicative of stress and fatigue, crime scene teams may be controlled and

managed to ensure that investigators avoid fatigue and the subsequent decline in cognitive function. This literature

review aims to address the suitability of wearable technology in the above scenarios.

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3 LITERATURE REVIEW

3.1 INTRODUCTION

Wearable devices have grown in popularity in recent years, with sales expected to total 340 million units in

20181. Watches, bands, glasses and clothing are just some of the applications that have come out in recent

years from companies such as Fitbit, Garmin and Apple2. They have been proposed as solutions to many

modern issues. Two potential applications are crime scene investigation, in the areas of monitoring fatigue

and stress, as well as tracking investigators throughout the crime scene.

While the stressful and fatiguing nature of police work is well documented3-8, the effect of stress and

fatigue on crime scene investigation remains under-researched. Policing is similar to crime scene

investigation, however, there are enough fundamental differences between the two roles, that crime

scene investigation should be studied seperately9.

There are many factors which predispose crime scene investigators to fatigue and stress, including long

working hours, less than ideal working conditions and traumatic scenes8, 9. It is possible that this may have

an effect on their efficiency and productivity in the workplace. Stress and fatigue have been demonstrated

to have an adverse impact on an individual, not only health-wise but also on their productivity in the

workplace10. Detecting when an individual is experiencing high levels of stress and fatigue could prevent

errors, thus ensuring the integrity of the investigation is maintained11.

There are two types of methods for detecting symptoms of fatigue and stress: self-reporting surveys and

physiological measures. Physiological measures can include blood pressure, heart rate, body temperature

and oxygen saturation levels12. A self-reporting survey can be used to correlate the physiological data

given so that when the physiological measures indicate stress or fatigue, they can potentially be matched

with moments of stress or fatigue reported in surveys.

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Wearable monitors are equipped to obtain many of the physiological measures of stress and fatigue.

Smartwatches, such as the Apple Watch and Garmin fēnix 5, are regularly embedded with heart rate

sensors to detect heart rate data13. Wearable blood pressure monitors have also recently been developed,

and validated, whereas portable pulse oximeters have been widely available for some years. A recent

breakthrough is the development of temperature monitors in the form of a wearable patch, used to

monitor babies14. All of these wearable devices have the potential to be adapted for use in monitoring

fatigue and stress.

The other potential application for wearable devices involves the overhaul of the crime scene log. Crime

scene logs are used by investigators to log the movements of all authorised personnel in and out of a

cordoned crime scene15. These are completed by hand or digitalised15. They must be a complete and

accurate representation of the movements of all personnel to ensure integrity is maintained. While there is

limited study available on the accuracy of these documents, their reliability could be questioned, due to

the possibility of human error. This could be improved by implementing a geotracking system using

wearable devices, for crime scene investigators.

Today, most devices have access to some form of positioning system, most commonly GPS (Global

Positioning System)16. Smartphones, smartwatches and other applications all make use of this satellite

technology, with smartwatches like the Garmin fēnix 5 employing both GPS and GLONASS (Global

Navigation Satellite System) satellite systems to improve tracking accuracy16. These devices have numerous

applications, from monitoring the mobility of elderly people17 to tracking sports stars on the field18, and

also could potentially be useful in eradicating the need for a crime scene log, if accuracy is demonstrated,

satellite-enabled devices could be used to monitor the movements of crime scene personnel.

The importance of crime scene investigation is evident in its integral role in the justice system19. The recent

so-called ‘CSI effect’, where jurors and prosecutors expect forensic evidence in the court process, means

that the court expects high standards from evidence collected by crime scene investigators20. Any errors in

the collection of evidence can then have an adverse flow-on effect, and could ultimately culminate in a

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miscarriage of justice21. Therefore, any additional apparatus that supports this vital process should be

considered. As crime scene investigators experience fatigue and stress, and as the efficiency and accuracy

of their notes could be improved by replacing a crime scene log with wearable geotracking devices, these

avenues should be explored.

This literature review will address the application of wearable devices in aiding crime scene investigation in

two ways: by detecting indicators of stress and fatigue in crime scene investigators, and replacing a crime

scene log with data gained from continuous GPS monitoring using a smartwatch.

3.2 WEARABLE DEVICES

A wearable device can be defined as an appliance that: contains a central processing unit, runs programs

that can be controlled by the user and are worn on a part of the body22. These can take many forms,

including glasses23, watches, bands and clothing. Wearable technology also has a wide variety of functions,

from measuring biophysical metrics such as heart rate, temperature and blood pressure, to GPS

positioning.

Smartwatches are one such modern wearable device and are expected to make up 60% of the forecast $25

billion wearables market in 201924. The Apple Watch®25 and the Garmin fēnix® 526 are popular examples.

They have been proposed for healthcare monitoring, due to the ease of use and unobtrusiveness13.

Simplistic devices like the Fitbit Charges™ have found to be effective at collecting the number of steps

taken, efficiently quantifying activity levels27, 28. Awoulusi et al. examined various types of wearable

technologies physiological data for monitoring safety in the high-risk industry of construction29.

Physiological monitoring, environmental sensing and location tracking were some of the applications

considered. After evaluating accuracy and security, it was concluded that wearable devices are suited for

personalised safety monitoring. Griffiths et al. came to a similar conclusion in their study, finding that

wearable devices could detect indicators of fatigue in the workplace27.

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Various sources have examined the factors that may influence a user in adopting wearable technology30-32.

A common concern is the privacy of data accumulated by the wearables31, 33. Many companies share the

locations of users, as well as other private information31. While this information has been ‘anonymised’,

i.e. stripped of the user’s name, this data can still be identifying due to the advent of algorithms that can

cross-reference location with other available data31. The recent so-called ‘Strava leak’ reveals the dangers

associated with seemingly innocuous information that is readily available. In November 2017, fitness

company Strava released a global heat map displaying 1 billion user activities, compiled over two years34.

Examination of the data presented revealed the potential location of military bases around the world,

through logging the anonymised exercise of personnel who used the app because the predominant users

of the app in these locations are military foreigners35. It was also shown that users could be searched

based on their commonly used routes, therefore enabling the possible identification of military

personnel36. These privacy concerns should be taken into consideration, however, with strong encryption

measures and authentication, data can be secured29.

Despite privacy concerns, wearable technology is advancing at a rapid rate37. It is anticipated that what is

currently available as separate devices will, shortly, be available in one38. Therefore, by using different

devices could be one day replicating what may be one day combined into one device. This device could be

useful in the various application of crime scene investigation. Baber et al. have been prolific in their use of

wearable and mobile technologies to crime scene investigation39 40. Their potential applications to modern

crime scene investigation will be discussed throughout this document.

3.3 FATIGUE

Fatigue can be defined in many ways. In a study by Basinska et al. it is referred to as a subjective state in

which an individual’s inclination and ability to take on further physical and mental effort is diminished41.

Alternatively, it can be defined as the state of muscles and the central nervous system in which prolonged

physical activity or mental processing, in the absence of sufficient rest, leads to insufficient capacity or

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energy to maintain the original level of activity and or processing42". There are also several types of fatigue:

physical fatigue, emotional fatigue, and mental fatigue.

3.3.1 Mechanism

Physical fatigue, which can be defined as the reduction in the maximum capacity of muscle to generate

force, is caused by severe physical activity43. There are two primary physiological mechanisms of fatigue:

neural fatigue, which is the inability of the related nerves to sustain a signal; or metabolic fatigue, the

incapacity of a muscle fibre to contract44.

Emotional fatigue is the chronic state of physical and emotional depletion resulting from a heavy

workload45. It presents as a combination of physical fatigue and a self-reported feeling of emotional

depletion46 and if it is not addressed with adequate coping strategies, burnout results47. Little is known,

however, about the neuronal mechanism of emotional fatigue47. A heavy workload, stressful environment

and overtime are key risk factors for emotional fatigue in employees41.

Mental fatigue, a reduction in cognitive ability as a result of prolonged activity48, occurs by a complicated

neural mechanism that is poorly understood49. It has been demonstrated, however, that individuals often

report an increase in fatigue after physical exertion, followed by a perceived reduction in cognition50.

Komaroff et al. found that in patients with Chronic Fatigue Syndrome, cognition was diminished in 70% of

patients51. Kujala et al. used attention span testing to test cognitive fatigue in patients with multiple

sclerosis, finding that motor and cognitive-related fatigue was induced in all groups at the conclusion of the

activities 52. However, as the studies above have compiled these studies using patients who were already

ill, reduction in cognitive abilities may be due to other factors associated with disease44. This can be

disputed by Bryant et al., as well as Krupp et al., however, both of whom compared cognitive fatigue in

both healthy individuals and those affected by multiple sclerosis53, 54. Both studies found that, although

those affected by the degenerative disease fatigued quicker than the healthy controls, the reduced

cognitive ability was experienced by all fatigued participants53, 54.

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As workers involved in crime scene investigation work long hours in less than ideal conditions, and

encounter all three types of fatigue55, it can be presumed that fatigue may influence cognition in crime

scene investigation to some degree.

3.3.2 Fatigue and crime scene investigation

The risks of fatigue-related problems are particularly high for both police officers and crime scene

investigators5, as they both experience similar stressors. Crime scene investigators encounter many of the

stressors involved in police work. Firstly, work hours for police are not standardised and regulated like in

other industries5, leading to long shifts and excessive overtime. Long work hours are a known

predisposition to fatigue, as excessive work demands lead to exhaustion41, 56. Police work is also highly

unpredictable. The unpredictable nature of police work is also conducive to fatigue57.

Crime scene investigators, however, interact with the crime scene more intensively and for an extended

period of time9. Crime Scene Investigators are regularly exposed to extreme weather conditions for long

periods of time. Physical fatigue is known to be exacerbated by heated weather conditions58and cold

weather, due to body heat loss 59.

Fatigue is a concern in the workplace for several reasons. Firstly, as demonstrated above, fatigue has been

associated with reduced cognitive ability49. This reduction in ability could be linked to a decrease in

performance, culminating in errors. Morris et al., investigated fatigue in sleep-deprived pilots, correlating

mistakes with subjective fatigue levels and physiological measures60. They noted more errors overall after

sleep deprivation60, a finding that was supported in a study by Paul et al. who found that decreased sleep

was associated with reduced subjective alertness and increased physical and mental fatigue61. Criticism of

these findings can be seen in the definition of fatigue used by the studies, which was a subjective feeling of

‘sleepinness’62. Duntley claims that excessive sleepiness and fatigue overlap, but are intrinsically different

and arise from different neurological mechanisms62. Despite the debate as to the extent, there is evidence

that reduced cognitive ability arising from fatigue results in some degree of impeded performance. As any

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error can ultimately be detrimental to the obtainment of justice, mental fatigue must be carefully managed

in crime scene investigatiors20.

Mental fatigue has been shown to preclude physical ability63 and has also been found to be a significant

cause of accidents in the workplace49. Driving while fatigued is also a significant cause of traffic accidents,

with 20 - 30% of traffic accidents are caused by fatigue in Australia64. The personal safety of crime scene

investigators is an additional reason to monitor their fatigue, in order to reduce these kinds of incidents.

The effect of fatigue on the work of police officers is a well-studied area. The studies of Vila et al. discusses

the risk factors for fatigue in police work, and the negative effect fatigue has on police work as a whole5, 8.

One such study examined the effect fatigue had on interactions with citizens and found that officers that

were less fatigued were more likely to resolve situations peacefully. There is, however, a lack of study on

fatigue and crime scene investigators, with only one study focusing the effects of fatigue on crime scene

personnel. Yoo et al. found that fatigue is correlated with instances of post-traumatic stress disorder in

Korean crime scene investigators57. The effect of fatigue on evidence collection in the crime scene is

unknown.

3.3.3 Monitoring fatigue in the workplace using wearables

When an occupation is physically and mentally demanding, it is essential to have a plan for combatting

fatigue. Research is beginning to emerge on the identification and monitoring of fatigue in the workplace

using wearable technology. In 2017, Griffiths et al. used wearable technology to test for signs of fatigue in

retail workers over the course of a workday. Step count and heart rate were measured, using a Fitbit

Charge HR®65 (see figure 1) and a Polar RS8020CX® fitness watch and chest strap respectively, and reaction

time were recorded to illustrate fatigue. It was found that wearable devices could replicate the results of

laboratory studies on fatigue, but, called into question the practicality of the chest strap for the generation

of heart rate data due to the discomfort it caused to participants27.

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Another study related to wearable devices and fatigue in the workplace was conducted by Maman et al66.

Like Griffiths et al. they also used a Polar chest strap to monitor heart rate but examined physical fatigue

using an inertial measurement unit, the Shimmer3, which comprises of an accelerometer, gyroscope,

altimeter and a magnetometer. Participants were measured while completing tasks reminiscent of manual

handling in a warehouse assembly line. This study focused more on attempting to model workplace fatigue

compared to Griffiths et al.’s attempts to use devices to monitor employee’s level of fatigue. The study

itself is still evidence of proof of concept, in that it is possible to gain meaningful data on fatigue from

participants wearing wearable activity monitors66.

A Masters thesis study conducted by D’Souza used wearables to monitor fatigue, as well using GPS to track

fatigue67. The proposed research is based on this methodology, examined students who were conducting

an intensive body recovery exercise in conditions Garmin fēnix 5 was used to monitor. As the findings were

inconclusive, this study hopes to further examine the potential role of wearable monitors in the fatigue

management of crime scene investigators.

3.4 STRESS

Maintenance of the human body’s predefined constant state, or homeostasis, is critical to the wellbeing of

the individual68. Stress, while considered an indefinite term with many differing definitions, can be defined

as anything that threatens this homeostasis, be it physiological or psychological69, 70.

3.4.1 Mechanism

Despite the magnitude of research undertaken on the subject, the exact physiological mechanism of stress

is still poorly understood68. Broadly put, the human body responds to external environmental stressors

processed by the amygdala by activating two systems: the autonomic system and the hypothalamic-

pituitary-adrenal (HPA) axis. The autonomic nervous system is also activated71. This comprises of two

branches: the sympathetic nervous system (SNS), which induces the so-called flight or flight response and

the parasympathetic nervous system (PNS), which returns the body to a state of rest once the stimulus has

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been removed69. As a result of distress signals from the brain, the SNS branch is activated, while the PNS is

inhibited69. The SNS signals to the adrenal glands, which release the hormone epinephrine (adrenaline),

increasing heart rate and blood pressure. The HPA axis is activated in response to sustained stress and

results in the release of cortisol, which suppresses the immune system12.

The effect of stress on cognitive ability is debated. There are several hypotheses, the first being that

subjects under the influence of a stressful environment perform less well in comparison to those in a calm

environment72. Jamal et al. found a negative linear relationship between stress and performance, attesting

to the existence of an alternate stress model, in which more stress results in better performance73. As

evidence exists for both sides of the debate, the relationship between stress and cognitive ability remains

undetermined73.

3.4.2 Stress in Crime Scene Investigation

Stress is a growing problem in modern society, with reported stress levels rising in the workplace74. In

2016, the American Psychological Association found about 25% of American people experience high levels

of stress75. High levels of stress in the community is known to have an enormous economic impact, through

decreased productivity and high use of health care services76. When a person becomes stressed, fatigue

and increased errors often result77.

The workplace, in general, is a stressful place78, however some workplaces are more stressful than others9.

A key stressor is a dense, prolonged workload; others include even interpersonal employee relations or

fear of injury78. Police work is well-documented as a stressful job4, 79, 80. A principal source of stress, long

working hours, is an intrinsic part of police work and crime scene investigation8.

Although forensic investigators have similar work environments to police, their fundamental differences

mean that they should be examined in separate studies77. At present, the majority of the literature focuses

on determining levels of stress faced by police officers in general; comparatively fewer studies on stress in

crime scene investigators have been conducted81-83. One such study showed that crime scene investigators

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are exposed to additional stressors, such as more visceral and frequent exposure to traumatic scenes, in

comparison to patrol officers84. From the perspective of stress, crime scene investigators are exposed to

fewer stressful environments, but as they remain in them for a lengthier period, these result in higher

levels of stress9.

The training of a crime scene investigator may also influence the type of stress faced. The training required

for the role of crime scene investigator varies widely, with some organisations requiring a sworn officer in

the position, with others employees civilians with relevant university degrees or formal training85. This may

mean that stress levels could vary between organisations, as well as among individuals who have had

different training to fill a similar role9. While there is little research on this topic, Dollard et al. found a

marked difference between the stress perceptions of sworn and non-sworn crime scene investigators3,

which was collaborated by a study conducted by Leone et al9. Holt et al., however, found no difference in

the level of stress in sworn and unsworn forensic personnel81. However, as this study primarily focused on

laboratory-based forensic scientists the transferability of this finding to field-based forensics operatives81.

The disparity in environment between laboratory-based scientists and field-based crime scene

investigators could significantly affect their respective perceptions of stress, and should not impede the

investigation into the differing perceptions of stress in sworn and unsworn officers.

Another stressor is the pressure for perfect work on behalf of a forensic investigator77, 86. This is due to the

nature of forensic work, in which mistakes can have enormous implications in court. The forensic science

field has also been criticised recently in reports such as the PCAST report87. In a review of work stress in

forensic personnel conducted by Jeanguenat et al., professional criticism was indicated as a key source of

increased workplace pressure77.

3.4.3 Monitoring stress in crime scenes using wearables

Some studies have been conducted in an attempt to quantify workplace stress experienced by crime scene

investigators. Adderley et al.83 individually examined four civilian crime scene investigators by measuring

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their mean above-average resting heart rate, which is the difference between their heart rate at intervals

throughout and their lowest recorded heart rate. To conduct the trial, participants wore a physiological

monitoring device (PMD) in the form of a belted shoulder strap. Heart rate was captured once a second

over the 8-hour shift and activity was logged.

This study shows that increased stress is present over the course of a shift, even during routine scenes such

as home burglaries. This study demonstrates that increased stress is present and postulates that this may

negatively affect the recovery of evidence from a crime scene. Adderley et al. used ‘mean above-average’

resting heart rate to determine when crime scene investigators were experiencing stress83.

These studies concentrate on showing evidence of stress in crime scene investigators. However, it would

be beneficial to go further than recognising that crime scene investigators experience higher levels of

stress. It would be ideal to be able to continuously monitor a crime scene investigator for signs of adverse

stress effects. The use of a minimally impactful wearable would be suitable for this purpose, unlike the one

used by Adderley et al. (figure 1). The idea is a novel one, with limited research, indicating the need for a

study in this area.

3.5 GAUGING STRESS AND FATIGUE IN INDIVIDUALS

There are two predominant ways to measure indicators of fatigue and stress: self-reported scales and

physiological data.

Numerous fatigue scales have been published, focusing on different aspects of fatigue and tailored

towards populations. Popular examples include the Perceived Stress Scale88 for stress and Chalder’s fatigue

scale for quantifying fatigue89. Advantages of measuring symptoms of fatigue using these scales include:

they are inexpensive, quick to complete and require very little thinking 44. They are, however, vulnerable to

both self-report biases, which describe the tendencies of data given by the participant to be misleading or

inaccurate due to various social pressures90; as well as recall bias, which is the tendency of an individual to

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recall feelings or events incorrectly as time passes91. Recall bias has been shown to be significantly reduced

if the response is recorded contemporaneously92, through the use of a wearable device, for instance.

An alternative way of attempting to map fatigue and stress in an individual is to use so-called performance

measures or physiological data. The data is independently verifiable and can be seen as a more ‘reliable

measure of ’93. Traditional methods of obtaining measures of fatigue, such as using an Electrocardiogram

to measure heart rate variability, are expensive and not suitable for continuous monitoring of individuals

over long periods of time, due to the cumbersome nature of the devices. Use of such devices also requires

specialised training and laboratory conditions. Developments in wearable technology could prove however

to overcome these limitations.

Stress and fatigue may be gauged by monitoring the following physiological metrics: blood pressure, heart

rate, body temperature and saturated oxygen levels. Below, the mechanism for each will be explained, as

well as the devices, both traditional and wearable, that can be used to monitor them.

3.5.1 Blood Pressure

Blood pressure can be defined as the force exerted by blood on a blood vessel wall12. When the left

ventricle of the heart contracts, forcing blood into the aorta, the aortic pressure reaches a peak known as

systolic pressure. When the aortic valve closes, the walls of the aorta recoil, and the lowest level of aortic

pressure, known as diastolic pressure, is reached. Blood pressure is expressed in the form of the systolic

measurement, over the diastolic measurement, in mm of mercury12. Normal ranges include a systolic of

less than 120mm Hg and a diastolic of between 70 – 80mm Hg12, while readings of above 140mm Hg

systolic and 90mm Hg diastolic are considered high94.

Stress is known to influence blood pressure95. This is because cortisol, a hormone produced when an

individual is placed in a stressful environment, is a key regulator of blood pressure96. Evidence of this can

be found in those with workplaces of high stress: many studies have found that those who worked in such

workplaces had a higher blood pressure97. Research has focused on quantifying to what degree stress

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influences blood pressure. Vrijkotte et al. monitored the blood pressure of office employees over two

workdays, identifying those in high-stress environments through self-reporting questionnaires. Over the

course of the study it was demonstrated that, in a stressful work environment, participants recorded a

significantly higher systolic blood pressure, which persisted into periods of rest98. This is corroborated by

the results of similar studies on blood pressure and work stress by Schnall et al97, 99. It can, therefore, be

concluded that it is possible to detect stress using blood pressure.

Blood pressure can be measured in many ways. For many years, the gold standard has been the

auscultatory method, where blood pressure is measured by occluding the brachial artery using a mercury

sphygmomanometer. The cuff is fitted to the upper arm securely, close to the elbow, and the cuff is

inflated. The individual completing the measurement will then reduce the inflation gradually, recording the

systolic pressure measurement when soft tapping sounds, i.e. Korotkoff sounds, are first heard, and

diastolic pressure when these sounds disappear entirely. This method is, however, on the decline due to

the ban on mercury-based devices100. It is also known to be affected by many factors, including room

temperature, the positioning of the arm, and anxiety101.

Oscillometric methods, using automated devices, are now more commonly used to measure blood

pressure, due to their low cost, ease of use and comparable accuracy100, 101. An inflatable cuff is positioned

on the upper arm, and oscillometric pulses are measured using a pressure sensor. This method operates on

the assumption that the point of maximal pressure oscillation in a cuff correlates with mean arterial

pressure100. Correlation in oscillation with systolic and diastolic pressure are less defined, and translation of

these values through an algorithm is required to produce a traditional blood pressure measurement102.

These algorithms vary between devices, but, in general, accuracy remains constant103, 104.

Blood pressure monitors have been made wearable through the QardioArm105 and Nokia BPM+106 blood

pressure monitors. These devices use oscillometric measurement and are controlled by an application on a

smartphone. These are advantageous due to the capacity to store results. Both the QardioArm and Nokia

brands of wearable blood pressure monitors have been externally validated and to meet the European

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Society of Hypertension International Protocol, making them acceptable for use by the general

population107, 108

3.5.2 Heart Rate

Heart rate is the speed at which the heart beats, measured in beats per minute12. Consensus varies as to

what an average, resting heart rate is, but it is described as between 50 to 90 beats per minute109, 110. A

fast heart rate, or tachycardia, is above 100 beats per minute, while a slow heart rate, bradycardia, is

below 50 beats per minute.

Stress is known to elevate heart rate111. A study by Vrijkotte et al. demonstrated that high heart rate is

correlated with a high-stress workload, which persisted after completion of work98 Therefore, by

measuring a person’s heart rate, it is possible to determine if they are stressed.

Fatigue can also be estimated using heart rate. Griffiths et al. examined using heart rate variability to

monitor fatigue in the workplace27. Heart rate variability is defined as the period between successive heart

beats112, 113. Shorter intervals between heart beat can be indicative of a demanding task of some form,

whereas longer intervals indicate periods of rest27. While heart rate variability is an insightful measure, it

can be difficult to use in a workplace environment due to the complexity of analysis114. Mean heart rate is

a commonly used tool to identify increased mental workload115.

Heart rate can be determined using electrocardiography (ECG)116. This uses electrodes to measure the

electrical changes in the activity of the heart, or the magnitude and direction of depolarisation, changes in

electric charge distribution, throughout the cardiac cycle. The output of monitoring is given as a graph of

voltage over time and called an electrocardiogram 117. ECG measurements can be affected by motion,

leading to a false diagnosis of an irregular heartbeat. Interpretation of an electrocardiogram requires a

trained technician and laboratory conditions, making it incompatible with continuous, real-life,

monitoring117.

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Wireless heart rate monitors utilise electrocardiography to record the electronic activity of the heart116.

Chest straps are currently commonplace and are relatively cheap, however, the accuracy of a chest strap

can be affected by a lack of moisture between the skin and electrode, and electrode gel is sometimes

required to ensure conductivitiy27. They can also be cumbersome and uncomfortable to wear for long

periods83.

Choi et al., 2009, used only heart rate data to detect activation of the two parts of the Autonomic Nervous

System: the Sympathetic Nervous system (SNS – fight or flight response) and the Parasympathetic Nervous

System69. When the SNS is activated, raising heart rate, however when the PNS is activated, heart rate is

lowered. The difficulty is establishing the cause of changes in heart rate. A decrease in heart rate, for

example, could be the result of an increase in activation of the SNS, or a decrease in the activation in PNS.

To establish which part of the ANS was active in changes in heart rate, a nonlinear system identification

technique was used. Heart rate data was gathered from participants engaged in mentally demanding and

non-mentally demanding tasks and was able to predict stressful events 83% of the time. Unlike a similar

study by Zhong et al., which used pharmacological blockade, i.e. using a drug to block the effect of a

neurotransmitter, and ECG measurements, the sensors used were consumer grade118.

Another common measure of heart rate are optical heart rate sensors, which are found in wearable

devices such as Fitbits28. These utilise a low-cost method known as photoplethysmography to measure

heart rate. In its most basic form, photoplethysmography requires only a light source to illuminate the skin

and a photodetector to detect blood volume changes at the microvascular level119. As light is refracted

differently by different volumes of blood, a pulsatile waveform is produced, which can be aligned to

heartbeat119. This is because blood flow is inversely proportional to the amount of light refracted120.

Photoplethysmography has been validated as a technique to measure heart rate, however accuracy has

been known to slip when exercise is more intense119-123. This is most likely due to movement disturbing the

sensor. While manufacturers of these devices develop algorithms to counteract the disturbance caused by

movement, there is an inherent degree of inaccuracy119. Spierer et al. also noted that a device using

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photoplethysmography can be affected by the skin type of the wearer120. High concentrations of melanin

lead to low photosensitivity, thus influencing the sensitivity of a photodetector124. Spierer identifies this as

significant weakness in research into the accuracies of such devices120. Hwang et al. compared a wristband

that employed a photoplethysmography sensor and a chest strap fitted with electrocardiograph sensor in

their ability to accurately monitor the heart rates of construction workers125. This was found to be useful in

the capture of heartrate data, and is expected to help workers construct rest strategies to ensure safety in

the workplace.

3.5.3 Body Temperature

Maintaining a normal body temperature is essential to bodily homeostasis. Average body temperature

ranges between 36.5–37.5 °C, but can be affected by many factors, including age, sex and time of day126.

Stress has been shown to affect body temperature. A study by Vinkers et al. showed consistent body

temperature changes from induced stress, although the effect varied according to where temperature is

measured from127. It was found that intestinal temperature decreased, which could be decreased intestinal

activity in a stressful environment. The study also found that stress affected the temperature as measured

on the skin of participants, with decrease distally, i.e. temperature decreased the further down the arm it

was measured. Oral and axillary temperature, however, has been shown to increase in stressful

situations128-130. Although none of these studies specifically refer to tympanic temperature, it may be

presumed that their effect will be similar due to the correlation that exists between oral, axillary and

tympanic126. The findings presented mean that stress could potentially be measured using body

temperature.

Vergara et al. examined the link between body temperature and alertness by measuring participants body

temperatures while involved in intensive mental activity. It was found that, while tympanic and forehead

temperatures remained constant throughout, finger temperature decreased in response to the cognitive

loading131. This concurs with the findings of reduced finger temperature by Vinkers et al., though it is

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difficult to determine whether the decrease is due to stress or mental fatigue, or a combination of both.

Gonzalez-Alonso et al., however, demonstrated that physical fatigue is linked to elevated body

temperature, by monitoring the oesophagal temperature of individuals performed randomly assigned

activities of cycling until complete exhaustion58. The difference in findings between the studies may reflect

the differing mechanism of physical and mental fatigue.

Body temperature can be measured by many methods in a variety of locations, including the mouth,

rectum, ear and the armpit. Temperature will vary according to the site of measurement126, with orally

measured temperatures measuring about 0.4°C less than rectal or tympanic measurements132. Body

temperature can be measured via a tympanic thermometer, such as the Braun™ Thermoscan® (see figure

4), An infrared probe is inserted into the ear canal and measures the which measures the temperature of

the blood vessels of the ear canal. This is a method which has been consistently validated133-135 but is

invasive and obstructive for continuous monitoring of individuals.

Recent advances in technology, however, have the development of seen the rise of temperature patch

monitors, such as the Nurofen FeverSmartTM (see figure 5)14. Usually used to monitor children, their

potential to monitor individuals in the workplace to monitor fatigue or stress has not been explored. While

the Nurofen FeverSmart has not been validated, a similar device, called the FeverFrida, has been shown to

meet the ASTME1112-00 standard, which gives a specific range requirement for temperature

measurements126.

3.5.4 Saturated O2 Levels

Saturated O2 levels are given as the percentage of haemoglobin molecules in the blood which is saturated

with oxygen. It can be obtained by measuring the concentration of oxygen in an arterial blood sample,

giving values in mm Hg, or it can be estimated by a pulse oximeter. Pulse oximeters operate similarly to

photoplethysmography orientated heart rate monitors, in that small light-emitting diodes, one red, the

other infra-red, is emitted through the patient’s fingertip or earlobe. Oxygen bonded haemoglobin absorbs

more infrared light than non-oxygen bonded haemoglobin, while deoxygenated haemoglobin absorbs

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more red light. The amount of light that is not absorbed is measured by an electronic processor and a

percentage. While pulse oximeters are not as accurate as arterial oxygen measurement, they are similar

enough that their non-invasiveness makes these suited for clinical use138.

Normal levels of saturated oxygen are given at between 95 to 100 percent. Hypoxemia, abnormally low

levels of oxygen saturation, occurs when levels fall below 90 percent. This can result in breathlessness,

and, in severe cases, respiratory failure.

Oxygen saturation is affected, over time, by an individual’s respiratory rate. As stress alters the respiratory

rate, it is possible that oxygen saturation will also be affected139. Crime scene investigators also wear

respiratory protective masks continually throughout an investigation to avoid contamination140. These

impede respiratory rate, making it probable that levels of oxygen saturation are lowered over time.

Performance is known to be affected by respiratory masks, especially when combined with protective

clothing141. Saturated O2 levels may be used to detect this decline in performance.

Wearable pulse oximeters can be worn on the fingertips138, earlobe, and in the form of a wristwatch142.

Fingertip pulse oximetry has been in use for some time and has been validated138.

Various wearable devices can track all of the above physiological measures, making them potentially suited

for monitoring fatigue and stress in crime scenes.

3.6 LOCATION TRACKING OF INDIVIDUALS AND OBJECTS

The position of an individual or an object at any given time can be immeasurable valuable in a large range

of situations. Modern advances in technology have provided numerous methods and devices with which to

accomplish this, including RFID tracking and GNSS-enabled devices.

3.6.1 RFID Tracking

Radio Frequency Identification Technology, or RFID, allows identification of objects from a distance,

without needing the object to be directly in view143. They can be classified as either active or passive, with

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active RFID tags requiring a power source. Passive RFID tags are small, have an indefinite operational life,

and comprises of an antenna and a semi-conductor chip 144.

An RFID system consists of multiple parts, as shown in figure 7. These include the RFID tag, a reader or an antenna,

middleware and Enterprise Applications. The RFID communicates via radio waves to the reader/ antenna. The raw

data is sent to middleware, which extracts information from the read data that is then transmitted to the designated

enterprise application145. An antenna or reader can be fixed or mobile, depending on the desired application of the

RFID system145.

3.6.1.1 RFID Applications

There are a wide variety of applications for RFID technology. One particularly high-profile application is in

sport. RFID chips were adopted for sporting events in the 1990s and are used mainly to time athletes in

running, cycling and other sporting events146. This is known as transponder timing and can be achieved

using either an active or passive transponder. Active systems have high read rates, i.e. a high level of the

data stored on the tag can be retrieved using wireless radio signals, and are precise in time-stamping, but

are predominately used in cycling, motor racing and events like the Olympics, due to their very high cost147.

In races, the antenna is placed at the starting and finishing lines and occasionally at midpoints. All athletes

are given a unique code in their RFID chip, which is used to differentiate the exact time each chip passes

the antenna146.

As of 2014, all players in the American National Football League are equipped with RFID chips in their

shoulder pads. This has produced a dataset called Next Gen Stats, that monitors the speed distance and

direction of each player148. Stadiums are set up with 20 receivers, which determines the player’s speed and

triangulates location to within six inches148. All data is then uploaded to a command centre and

analysed149. This type of information is useful in focusing on individual players and can be used to adjust

their training to ensure the team is all at peak performance.

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3.6.1.2 Crime Scene Applications of RFID

RFID tracking has the potential to be applied to crime scene investigation. Several sources have proposed

the use of RFID tags in the handling of evidence bags11, 145, 150. Baber et al. have proposed several methods

using an RFID chip to aid in the tracking of evidence. Williams et al. conducted a review of the subject area

and noted the barriers involved in implementing RFID. These include the significant initial cost in the

implementation of such a system and the perceived unreliability of RFID as a system145.

Few law enforcement agencies have implemented this technology for these reasons, however, the

Netherlands Forensic Institute has implemented an evidence tracking system151. An adhesive label,

containing an RFID tag and bar-coded serial number, is placed on the packaged evidence, and a

corresponding bar code is logged in an exhibit log. An RFID reader is installed at the Forensic Institute,

registering all the items of evidence as they go through. The evidence is then manually scanned, to ensure

complete maintenance of integrity. The Texas Country Fire Marshall uses a similar system, albeit on a

smaller scale, but also classify their evidence as requiring either high and low security152. Items needing

high levels of security, such as drugs, DNA and weapons are given active RFID tags that signal if they are

moved in any way. Evidence that is deemed unlikely to be stolen or not integral to a case is assigned a

passive RFID tag that will signal if removed from the office152.

RFID tags present a comprehensive solution to evidence handling in law enforcement. In regards to

monitoring individuals within crime scenes, however, a more sophisticated system is needed: RFID systems

require complex set up that may not be feasible to construct every time a crime scene is investigated144. A

positioning system that is portable and highly accurate, such as one or more of the Global Navigation

Satellite Systems, is more appropriate for this application.

3.6.2 Global Navigation Satellite Systems

A Global Navigation Satellite System (GNSS) is a system that utilises multiple satellites in various planes

around the globe to position an object on earth153. While there are various systems in place in the world

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today, only several have global coverage. These include the United States’ Global Positioning System (GPS)

and Russia’s (GLONASS)153.

3.6.3 GPS

Global Positioning System (GPS) is a widely used satellite navigation system established by the United

States in the early 1960s to determine position in three dimensions 154. The full operational capacity of the

GPS was reached in 1995, meaning all satellites were functional, and emitting signal. This consists of 24

satellites oriented around the Earth in 6 orbital planes which send data unconditionally. If an object is in

view of 4 satellites at any given time, this object can be tracked. GPS can be used independently of internet

connections; all that is required is a GPS signal receiver.

Each satellite broadcasts a signal that contains a code indicating its current time and position154. A GPS

receiver will receive a signal from multiple satellites (a minimum of 4) and derives its position using

equations. The location is given in three dimensions, i.e. latitude, longitude and altitude, together with the

current time. Satellites are controlled by several stations on the ground and overseen by the US Airforce,

but require limited maintenance from the ground over the course of their 7.5 to 11-year life span156. The

control segment of GPS consists of a master control station, ground antennas and monitor stations154.

GPS has two types of services: Standard Positioning Service (SPS) and Precise Positioning Service (PPS), for

military purposes. SPS is available for everyday ‘civilian’ usage, and is available on an unrestricted basis,

while PPS is a specialised positioning system designed for military purposes. SPS broadcasts signals at an L1

signal, containing the aforementioned navigation code156. L2 signals are used for the PPS155.

GPS receivers are tuned to receive this signal, using an antenna. Tracking a person’s physical location is

made possible through the use of GPS-enabled receivers, such as many modern smartphones, and is

referred to as Geotracking157.

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3.6.4 GLONASS

The Soviet Union commenced the Global Navigation Satellite System (GLONASS) in 1976 as an alternative

to GPS. It reached full capacity in 2010 and currently consists of 24 satellites arranged in a constellation of

19000km around the earth (see Figure 10).

GLONASS is setup similarily to GPS in that, in addition to the satellites, control from the ground is also

required. The GLONASS Ground segment is tasked with managing the GLONASS operation. It consists of

Command and Tracking Stations, which track the GLONASS satellites; a System Control Centre (SCC), which

manages the satellite constellation by processing information received from the Command and Tracking

Stations; Laser Ranging Stations, which provide estimations of accuracy; and a Central synchroniser, which

manages the timing aspect of GLONASS. Ground-based control of GLONASS satellites in managed by the

System Control Centre154.

A GLONASS enabled receiver is required to pick up the signal from GLONASS satellites. Companies such as

Septentrio Inc manufacture this receivers159, however GLONASS receivers are more commonly found

paired with GPS enabled devices, for reasons that will be discussed below.

GPS and GLONASS technology can be found on smartphone devices. Zandbergen et al. found that devices

that used GPS to position objects were more accurate than devices that used WiFi and cellular positioning

alone160.

3.6.5 The Accuracy of GPS and GLONASS

GPS signals from satellites must confirm, by law, to an error of less than, or equal to, 7.8 m horizontally

with a confidence rate of 95%, as a global average156. Frequently, however, it is found that the accuracy is

better than reported154, with the global average area in 2016 reported as less than or equal to 0.715m, at a

confidence interval of 95%155. Accuracy is reportable less in GLONASS system, ranging between 5- 10m in

latitude and longitude161

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Signal accuracy is not the primary factor of accuracy in GNSS devices, however. Differences in obtained

results can vary immensely from user to user, due to the variations between GNSS receiver configurations,

as well as environmental conditions154. A receiver must be able to pinpoint satellites, so buildings and trees

which can block satellite signal can lead to an incorrect position. Urban canyons, which is a city

phenomenon where a canyon is formed by large buildings flanking, can have a major effect on GPS

accuracy. Kaplan et al. found that in a moderate urban canyon position jumping, caused by signal blockage,

can be as much as 50 – 70m154. Implementing auxiliary sensors, such as gyroscopes, magnetic compasses

and accelerometers, may assist in reducing large position jumping154, 162. Additionally, major flaw in GNSS

systems is that they cannot be used accurately indoors16, 154.

The accuracy of GNSS systems can be improved by using augmentation systems. These can either be space

or ground based154. Wide Area Augmentation System (WAAS) is an augmentation system for GPS. It utilises

ground-based stations to measure variation in GPS satellites and sends correction messages to WAAS

satellites. GPS receivers that are programmed to receive WAAS signals can then use these corrections to

improve positioning accuracy163. WAAS must maintain a minimum accuracy of 7.6m at a 95% confidence

interval163.

Multi-GNSS describes the use of two or more Global Navigation Satellite Systems to improve the number

of satellites available for position tracking at any given time164. GPS and GLONASS, for instance, may be

used together. The use of multi-GNSS has been shown to improve the accuracy of positioning. Sarkar et al.

found that at positions given a small number of satellites, accuracy was improved by adding signals found

from GLONASS satellites164. Research from Truong et al. showed that GPS and GLONASS together

performed better than GLONASS alone, and marginally better than GPS alone165.

The most significant advantage of using is an increase in the number of satellites available to the receiver

at any given point166

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3.6.6 Current GNSS Applications

3.6.6.1 Elite Sports

At an elite sporting level, there is a demand for the kind of information that only location tracking devices

can provide. In Australian rules football, for example, GPS tracking has been used to monitor players during

play. A study by Wisbey et al. involved attaching football players to a shoulder harness containing a GPS-

enabled device, which they wore throughout several premiership games over some years. It was found

that the device could capture the following data points: total distance covered, maximum speed,

accelerations and a measure of exertion18. Coaches seek this kind of data points to improve training

programs, allowing them to keep up with the physical demands of the modern game. The accuracy of this

high-speed data generated by GPS systems was measured in a study by Rampini et al167. Participants were

tracked with two GPS devices, one at a frequency of 5Hz and the other at 10Hz while performing shuttle

runs. It was found that GPS accuracy increased with a higher sampling rate, only GPS-10Hz had the suitable

level of accuracy to enable this kind of analysis in elite sports.

GNSS is not limited to elite sports, however. It has also been suggested in clinical applications, such as a

means of monitoring the mobility of elderly population17, 168, is regularly used to track sex offenders169 and

also has applications in the workplace-environment, in employee monitoring 170. Crime scene investigation

is a workplace which could potentially benefit significantly from the use of GNSS-enabled technology.

3.6.6.2 Crime Scene Application

Very limited study has been undertaken on using GPS in conjunction with the work of crime scene

investigators. Baber et al. proposed using GPS as a method of comparing the way in which crime scene

investigators processed different types of crime scenes in different areas171. Their proposed methodology

includes plotting individuals’ movement onto a photograph of the scene and appending an explanation of

their movements. The purpose of this is to create a database on how to process similar crime scenes. The

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usefulness of this would primarily be limited to training purposes, however, and has very little applicability

to crime scene investigation as a whole.

Integrity is a necessary component of crime scene investigation, and the process of maintaining integrity is

an vital one20. One part of this is by carefully controlling and recording the personnel who enter the

scene20. All individuals are logged with identifying details, such as the name of the individual gaining entry,

their rank, vehicle registration number and the reason for entering the scene 15. Errors in crime scene logs

can, therefore, be detrimental to the integrity of a crime scene 20. As GNSS enabled devices can accurately

track the wearer in the outdoors, we propose another crime scene application for wearable devices: an

alternative for crime scene logs.

In a study using a wearable computer prototype, Baber et al. found that digitally imputing crime scene

notes, improved efficiency by a factor of 2, without negatively impacting the quality of notetaking. 40.

While no studies were found on the accuracy of crime scene logs, it could be assumed from this study that

replacing a crime scene log with an automatic geotracker would affect the accuracy, however, further

research is needed11.

The aforementioned thesis by D’Souza also examined the use of wearable devices as a means of replacing

a crime scene log67. It was concluded that this application is feasible and that in the pacing of 20 by 20m

square, GPS and GLONASS accuracy was found to be 2m. A similar methodology could be employed to

examine this gap in research further.

3.6.7 Geodetic Datum

A geodetic datum, also known as a geodetic system, is a set of coordinates used to locate points172.

Horizontal datums position a point on the surface of the earth, usually written in terms of latitude or

longitude, while vertical datums are used to measure elevation172. This study intends to use these geodetic

datums to assess the accuracy of GNSS enabled devices in geotracking crime scene investigators, as an

alternative to crime scene logs.

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3.7 AIMS OF THE PROPOSED STUDY

The literature review above has highlighted several gaps in the research which indicate wearable

technology may have a place in crime scene investigation. This study aims to use wearable monitors to

combine the monitoring of stress and fatigue in crime scene investigators with the tracking of their

geophysical location in the crime scene. This will aid significantly in ensuring the investigation runs

efficiently by ensuring cognitive faculties are maintained, the health of the investigator is being looked

after and paperwork reduced171. This study intends to investigate the usefulness of wearable activity

monitors in crime scene investigation. Monitors could be useful in two ways: to monitor fatigue and stress,

and to track investigators in and out of the scene. Therefore, the study aims to assess the ability of

wearable activity monitors in tracking the geolocation of and measuring the physiological indicators of

stress and fatigue in, crime scene investigators.

This aim can be divided into two smaller objectives:

1. To establish if physiological data generated by wearable devices can correlate with self-reported

instances of stress and fatigue.

2. To establish if the GPS/GLONASS system inside wearable devices can be used as a means of tracking

an individual in a crime scene, replacing the need for a crime scene entry and exit log.

3.8 HYPOTHESES

3.8.1 Hypothesis 1

H0 There will be no correlation between the physiological biometrics as measured by the wearable devices

of crime scene investigators and instances of stress and fatigue recorded in self-reported surveys.

H1: Instances of stress and fatigue reported in the surveys will coincide with an increase or decrease in the

physiological parameters measured.

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3.8.2 Hypothesis 2

H0: There will be no correlation between the data recorded in the crime scene log and the data obtained

through the satellite tracking capabilities of the Garmin fēnix® 5.

H1: The movements recorded in the handwritten crime scene log will be consistent with the data obtained

from the Garmin fēnix® 5

3.9 PROPOSED METHODOLOGY

This study will be conducted during a clandestine body recovery exercise undertaken by University Masters

students, enrolled in a practical forensic science course.

Students are formed into teams, with each member adopting one of the following roles: Crime Scene

Manager, Photographer, Mapper, Exhibits Officer and Logistics Personnel. In these roles, students are

tasked with finding, excavating and processing a pig buried in soil at Whitby Falls Farm (at 1619 South

Western Highway, Whitby, Western Australia), over two days, in a manner consistent with a real-life

investigation.

The study will monitor two of these participants, a male and female both in their early twenties, over the

entire exercise.

The participants will wear the following monitors:

- Garmin fēnix® 5 (a smartwatch, with GPS/GLONASS capabilities)

- Qardioarm (a wireless blood pressure cuff)

- Nurofen® Feversmart (a patch the measures body temperature, worn in the armpit).

- Nonin GO2 Achieve (a finger pulse oximeter for measuring saturated oxygen levels)

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3.9.1 Monitoring Physiological indicators of stress and fatigue

The participants will wear the above monitors to measure key physiological metrics: blood pressure, heart

rate, body temperature and saturated oxygen levels (SO2). The Garmin fēnix® 5 and the Nurofen®

Feversmart will be worn for the duration of the exercise, while measurements will be obtained from the

Qardioarm and the Nonin GO2 on an hourly basis. Self-reported surveys on stress88 and fatigue89 will be

conducted at the start and conclusion of the day, as well as during lunch. The physiological measurements

recorded will be compared to the results of the survey, and the correlation between changes in

measurements and self-reported instances of fatigue will be noted.

This exercise is conducive to fatigue and stress, due to numerous factors. Firstly, as the task is expected to

take place during early autumn, temperatures will still be sufficiently high enough173 that prolonged

exposure would exacerbate fatigue174 and heat stress175. Secondly, the working hours are long, from about

8 AM until 5 PM, which, as demonstrated above, is conducive to fatigue176. Thirdly, the task is cognitively

demanding, with students under time pressure to produce accurate results. The time pressure involved

with produce stress in the participants.

The Garmin fēnix® 5’s accuracy as a heart rate monitor will be calibrated against the heart rate data given

by the Nonin GO2 Achieve and the Qardioarm.

3.9.2 Geotracking Investigators

The Garmin fēnix® 5 will be used to geotrack participants throughout the body recovery exercise. The

smartwatch will have the ‘Track Me’ mode activated, which will record the geolocation of the wearer using

GPS and GLONASS satellites. The mode will remain activated for the duration of the exercise.

Both participants will keep a handwritten crime scene entry and exit log as a component of the exercise.

The satellite data gained from the Garmin fēnix® 5 worn by participants will be compared to these logs,

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and any differences highlighted. The accuracy of the satellite abilities of the Garmin will be assessed by

measuring a 20m square from a government validated datum point close to the site of the exercise172. This

will quantify the error involved in the Garmin’s tracking abilities.

This study requires the use of several activity monitors. Other wearables chosen include the QardioArm,

which measures blood pressure; FeverSmart™ adhesive patch to measure body temperature; and a Nonin®

pulse oximeter to measure saturated oxygen levels.

4 CONCLUSION

Crime scene investigation is a critical part of the criminal justice system. With modern-day trials placing

more and more importance on evidence collected during crime scene, It is critical that crime scene

investigators get all the support they need to complete their work safely and as accurately as possible.

Stress and fatigue as experienced by crime scene investigators are one barrier to the successful analysis of

a crime scene. There have been few studies on the effect each has had on the productivity of workers at a

crime scene. Wearable devices, when combined with self-reported surveys, could be used as a tool to

quantify stress and fatigue, as they have the potential to obtain indicators of these conditions. This could

improve the gathering of data from crime scenes, and improve the well-being of investigators. Therefore, a

study that assesses wearable devices for this purpose would be beneficial.

Many wearable devices have the added ability to utilise satellite tracking systems, such as GPS and

GLONASS. These functions also have potential forensic applications. In particular, geotracking capabilities

of smart devices such as the Garmin fēnix 5, could be used to improve the current methodology behind

crime scene logs.

As shown above, wearable devices can be used to improve crime scene investigation in measuring fatigue

and stress of investigators, as well as for the geotracking investigators. A study that encompasses the

evaluation of both of these functions would be beneficial to the field of crime scene investigation.

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5 REFERENCES

1. Gartner Says Worldwide Wearable Device Sales to Grow 17 Percent in 2017 [press release]. Egham, UK: Gartner, Inc., August 24 2017. 2. Fokkema T, Kooiman TJM, Krijnen WP, Van Der Schans CP, De Groot M. Reliability and Validity of Ten Consumer Activity Trackers Depend on Walking Speed. Med Sci Sports Exerc. 2017;49(4):793-800. 3. Dollard M, Winefield HR, Winefield AH. Occupational Stress in the Service Professions: CRC Press; 2003. 4. Sewell JD. The stress of homicide investigations. Death Stud. 1994;18(6):565-82. 5. Vila B, Kenney DJ. Tired Cops: The prevalence and potential consequences of police fatigue. Nij J. 2002;248. 6. James SM, Vila B. Police drowsy driving: predicting fatigue-related performance decay. Policing-an International Journal of Police Strategies & Management. 2015;38(3):517-38. 7. James L, James S, Vila B. The impact of work shift and fatigue on police officer response in simulated interactions with citizens. Journal of Experimental Criminology. 2018;14(1):111-20. 8. Vila B. Impact of long work hours on police officers and the communities they serve. Am J Ind Med. 2006;49(11):972-80. 9. Leone M, Keel R. Occupational Stress and the Crime Scene Investigator2016. 10. Karasek R, Theorell T. Healthy work: stress, productivity, and the reconstruction of working life: Basic Books; 1990. 11. Baber C, Smith P, Panesar S, Yang F, Cross J, editors. Supporting Crime Scene Investigation2007; London: Springer London. 12. Marieb EN, Hoehn K. Human Anatomy & Physiology: Pearson; 2013. 13. Lu T-C, Fu C-M, Ma MH-M, Fang C-C, Turner AM. Healthcare Applications of Smart Watches. Appl Clin Inform. 2016;07(03):850-69. 14. Nurofen(R). Nurofen FeverSmart Temperature Monitors: Reckitt Benckiser; 2017 [cited 2018 April 3]. Available from: http://www.nurofen.com.au/feversmart/. 15. ACPO. Murder Investigation Manual [Internet]. Wyboston: National Centre for Policing Excellence; 2006 [cited 2018 February 20]. Available from: https://www.app.college.police.uk/app-content/investigations/linked-reference-material/. 16. Hoelzl G, Kranz M, Schmid A, Halbmayer P, Ferscha A, editors. Size does matter - positioning on the wrist a comparative study: SmartWatch vs. SmartPhone. 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops); 2017 13-17 March 2017. 17. Shoval N, Auslander G, Cohen-Shalom K, Isaacson M, Landau R, Heinik J. What can we learn about the mobility of the elderly in the GPS era? Journal of Transport Geography. 2010;18(5):603-12. 18. Wisbey B, Montgomery PG, Pyne DB, Rattray B. Quantifying movement demands of AFL football using GPS tracking. J Sci Med Sport.13(5):531-6. 19. NFSTC. Crime Scene Investigation Guide: National Forensic Science Technology Center; 2013 [cited 2018 February 26]. Available from: https://www.nfstc.org/product/crime-scene-investigation-guide/. 20. Julian R, Kelty S, Robertson J. Get It Right the First Time: Critical Issues at the Crime Scene Special Issue: Forensic Science and Justice: From Crime Scene to Court and Beyond. Current Issues in Criminal Justice. 2012;24:25-38. 21. Ryan TG, Overlin TK, editors. Human reliability assessment: tools for law enforcement. Training, Education, and Liability Issues for Law Enforcement Scientists and Engineers; 1997: International Society for Optics and Photonics. 22. Shishoo R. Textiles in sport: Elsevier; 2005. 23. Muensterer OJ, Lacher M, Zoeller C, Bronstein M, Kübler J. Google Glass in pediatric surgery: An exploratory study. International Journal of Surgery. 2014;12(4):281-9. 24. Wearables Market to Be Worth $25 Billion by 2019 [press release]. CCS Insight2015.

37 | P a g e

25. Apple I. Apple Watch: Apple, Inc.; 2018 [cited 2018 April 10]. Available from: https://www.apple.com/watch/. 26. Garmin. Garmin fēnix® 5: Garmin; 2018 [cited 2018 April 10]. 27. Griffiths C, Bowen J, Hinze A, editors. Investigating Wearable Technology for Fatigue Identification in the Workplace. IFIP Conference on Human-Computer Interaction; 2017: Springer. 28. Takacs J, Pollock CL, Guenther JR, Bahar M, Napier C, Hunt MA. Validation of the Fitbit One activity monitor device during treadmill walking. J Sci Med Sport. 2014;17(5):496-500. 29. Awolusi I, Marks E, Hallowell M. Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Automation in Construction. 2018;85:96-106. 30. Adapa A, Nah FF-H, Hall RH, Siau K, Smith SN. Factors Influencing the Adoption of Smart Wearable Devices. International Journal of Human–Computer Interaction. 2018;34(5):399-409. 31. Piwek L, Ellis DA, Andrews S, Joinson A. The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med. 2016;13(2):e1001953. 32. Pitt L, Kietzmann J, Robson K, Plangger K, Treen E, Paschen J, et al., editors. Understanding the Opportunities and Challenges of Wearable Technology. Creating Marketing Magic and Innovative Future Marketing Trends; 2017 2017//; Cham: Springer International Publishing. 33. Rauschnabel PA, Ro YK. Augmented reality smart glasses: an investigation of technology acceptance drivers. International Journal of Technology Marketing. 2016;11(2):123-48. 34. Strava. Global Heatmap 2017. Available from: https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all. 35. Bogle A. Strava has published details about secret military bases, and an Australian was the first to know ABC Science: ABC 2018 [cited 2018 April 10]. Available from: http://www.abc.net.au/news/science/2018-01-29/strava-heat-map-shows-military-bases-and-supply-routes/9369490. 36. Russell J. Fitness app Strava exposes the location of military bases Techcrunch: Oath Tech Network; 2018 [cited 2018 April 10]. Available from: https://techcrunch.com/2018/01/28/strava-exposes-military-bases/. 37. Casselman J, Onopa N, Khansa L. Wearable healthcare: Lessons from the past and a peek into the future. Telematics and Informatics. 2017;34(7):1011-23. 38. Amft O, editor Smart Eyeglasses, e-Textiles, and the Future of Wearable Computing. Advances in Science and Technology; 2017: Trans Tech Publ. 39. Baber C, Smith P, Butler M, Cross J, Hunter J. Mobile technology for crime scene examination. Int J Hum Comput Stud. 2009;67(5):464-74. 40. Baber C, Smith P, Cross J, Zasikowski D, Hunter J, editors. Wearable technology for crime scene investigation. Ninth IEEE International Symposium on Wearable Computers (ISWC'05); 2005 18-21 Oct. 2005. 41. Basinska BA, Wiciak I. Fatigue and Professional Burnout in Police Officers and Firefighters. Internal Security. 2012;4(2):267-75. 42. Job RFS, Dalziel J. Defining fatigue as a condition of the organism and distinguishing it from habituation, adaptation, and boredom. Stress, workload, and fatigue. 2001:466-75. 43. Gandevia SC. Some Central and Peripheral Factors Affecting Human Motoneuronal Output in Neuromuscular Fatigue. Sports Med. 1992;13(2):93-8. 44. DeLuca J. Fatigue as a Window to the Brain: MIT Press; 2005. 45. Wright TA, Cropanzano R. Emotional exhaustion as a predictor of job performance and voluntary turnover. J Appl Psychol. 1998;83(3):486-93. 46. Zohar D. Predicting burnout with a hassle-based measure of role demands. Journal of organizational Behavior. 1997:101-15. 47. Golonka K, Mojsa-Kaja J, Popiel K, Marek T, Gawlowska M. Neurophysiological Markers of Emotion Processing in Burnout Syndrome. Front Psychol. 2017;8:2155.

38 | P a g e

48. Ackerman PL, Association AP. Cognitive Fatigue: Multidisciplinary Perspectives on Current Research and Future Applications: American Psychological Association; 2011. 49. Ishii A, Tanaka M, Watanabe Y. Neural mechanisms of mental fatigue. Rev Neurosci. 2014;25(4):469-79. 50. DeLuca J, Johnson SK, Ellis SP, Natelson BH. Cognitive functioning is impaired in patients with chronic fatigue syndrome devoid of psychiatric disease. Journal of Neurology, Neurosurgery & Psychiatry. 1997;62(2):151-5. 51. Komaroff AL, Buchwald D. Symptoms and Signs of Chronic Fatigue Syndrome. Rev Infect Dis. 1991;13(Supplement_1):S8-S11. 52. Kujala P, Portin R, Revonsuo A, Ruutiainen J. Attention related performance in two cognitively different subgroups of patients with multiple sclerosis. J Neurol Neurosurg Psychiatry. 1995;59(1):77-82. 53. Bryant D, Chiaravalloti ND, DeLuca J. Objective Measurement of Cognitive Fatigue in Multiple Sclerosis. Rehabil Psychol. 2004;49(2):114-22. 54. Krupp LB, Elkins LE. Fatigue and declines in cognitive functioning in multiple sclerosis. Neurology. 2000;55(7):934-9. 55. Chapman B. Personal Experience as a Crime Scene Investigator. In: Morahan E, editor. Murdoch University: Murdoch University; 2018. 56. Lerman SE, Eskin E, Flower DJ, George EC, Gerson B, Hartenbaum N, et al. Fatigue Risk Management in the Workplace. J Occup Environ Med. 2012;54(2):231-58. 57. Yoo Y-S, Cho O-H, Cha K-S, Boo Y-J. Factors Influencing Post-traumatic Stress in Korean Forensic Science Investigators. Asian Nurs Res (Korean Soc Nurs Sci). 2013;7(3):136-41. 58. González-Alonso J, Teller C, Andersen SL, Jensen FB, Hyldig T, Nielsen B. Influence of body temperature on the development of fatigue during prolonged exercise in the heat. J Appl Physiol. 1999;86(3):1032-9. 59. Young AJ, Castellani JW. Exertional fatigue and cold exposure: mechanisms of hiker's hypothermia. Applied Physiology, Nutrition, and Metabolism. 2007;32(4):793-8. 60. Morris TL, Miller JC. Electrooculographic and performance indices of fatigue during simulated flight. Biol Psychol. 1996;42(3):343-60. 61. Paul MA, Pigeau RA, Weinberg H. CC130 pilot fatigue during re-supply missions to former Yugoslavia. Aviation, space, and environmental medicine. 2001;72(11):965-73. 62. Duntley SP. 13 Fatigue and Sleep. Fatigue as a Window to the Brain2005. p. 209. 63. Marcora SM, Staiano W, Manning V. Mental fatigue impairs physical performance in humans. J Appl Physiol. 2009;106(3):857-64. 64. Safety NR. National Road Safety Strategy 2011-2020: Australian Government; 2011 [cited 2018 April 23]. 65. Fitbit Inc. Fitbit Charge Hr: Fitbit Inc,; 2018 [cited 2018 April 24]. Available from: https://www.fitbit.com/au/chargehr. 66. Sedighi Maman Z, Alamdar Yazdi MA, Cavuoto LA, Megahed FM. A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. Appl Ergon. 2017; forthcoming. 67. D'Souza V. Utilizing GPS to track crime scene investigators within a crime scene and monitoring their fatigue [Masters by Coursework]: Murdoch University; 2017. 68. Tsigos C, Kyrou I, Kassi E, Chrousos GP. Stress, endocrine physiology and pathophysiology: MDText; 2016 [cited 2018 April 1]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK278995/. 69. Choi J, Gutierrez-Osuna R, editors. Using Heart Rate Monitors to Detect Mental Stress. 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks; 2009 3-5 June 2009. 70. Schneiderman N, Ironson G, Siegel SD. STRESS AND HEALTH: Psychological, Behavioral, and Biological Determinants. Annu Rev Clin Psychol. 2005;1:607-28. 71. Ziegler MG. 50 - Psychological Stress and the Autonomic Nervous System A2 - Robertson, David. In: Biaggioni I, Burnstock G, Low PA, editors. Primer on the Autonomic Nervous System (Second Edition). San Diego: Academic Press; 2004. p. 189-90.

39 | P a g e

72. Hockey R. Stress and fatigue in human performance: Wiley; 1983. 73. Jamal M. Job stress and job performance controversy: An empirical assessment. Organizational Behavior and Human Performance. 1984;33(1):1-21. 74. U.S. Study Reveals Rising Stress at Work Driven by Politics, Artificial Intelligence and Pressure to Master New Skills [press release]. Udemy, Inc.2017. 75. The State of Our Nation: Stress in America [press release]. American Psychological Association2017. 76. Kalia M. Assessing the economic impact of stress — The modern day hidden epidemic. Metabolism-Clinical and Experimental. 2002;51(6):49-53. 77. M. Jeanguenat A, Dror I. Human Factors Effecting Forensic Decision Making: Workplace Stress and Well-being2017. 78. Williams C. Sources of workplace stress. Perspectives on Labour and Income. 2003;15(3). 79. Dabney DA, Copes H, Tewksbury R, Hawk-Tourtelot SR. A Qualitative Assessment of Stress Perceptions Among Members of a Homicide Unit. Justice Quarterly. 2013;30(5):811-36. 80. Terry WC. Police stress: The empirical evidence. Journal of Police Science and Administration. 1981;9(1):61-75. 81. Holt TJ, Blevins KR, Foran RWS. Examination of the Conditions Affecting Forensic Scientists’ Workplace Productivity and Occupational Stress. 2016. 82. Sollie H, Kop N, Euwema MC. Mental Resilience of Crime Scene Investigators: How Police Officers Perceive and Cope With the Impact of Demanding Work Situations. Criminal Justice and Behavior. 2017;44(12):1580-603. 83. Adderley R, Smith LL, Bond JW, Smith M. Physiological Measurement of Crime Scene Investigator Stress. International Journal of Police Science & Management. 2012;14(2):166-76. 84. Pavsic Mrevlje T. Coping with Work-related Traumatic Situations among Crime Scene Technicians. Stress Health. 2016;32(4):374-82. 85. Heames JT, Heames JT. Forensic Science Staffing: Creating a Working Formula. Forensic Science Policy & Management: An International Journal. 2011;2(1):5-10. 86. Sewell JD. Identifying and mitigating workplace stress among forensic laboratory managers. Forensic Science Communications. 2000;2(2):NA>-NA>. 87. President’s Council of Advisors on Science Technology. Forensic science in criminal courts: ensuring scientific validity of feature-comparison methods. Executive Office of the President of the United States Washington, DC; 2016. 88. Cohen S, Kamarck T, Mermelstein R. Perceived stress scale. Measuring stress: A guide for health and social scientists. 1994. 89. Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, et al. Development of a fatigue scale. J Psychosom Res. 1993;37(2):147-53. 90. Donaldson SI, Grant-Vallone EJ. Understanding Self-Report Bias in Organizational Behavior Research. J Bus Psychol. 2002;17(2):245-60. 91. Raphael K. Recall bias: a proposal for assessment and control. Int J Epidemiol. 1987;16(2):167-70. 92. Whalen CK, Jamner LD, Henker B, Delfino RJ. Smoking and moods in adolescents with depressive and aggressive dispositions: evidence from surveys and electronic diaries. Health Psychol. 2001;20(2):99-111. 93. Sundberg C. Physiological and Self-Report Instruments to Measure Fatigue in Older Adults. 2015. 94. Gabb GM, Mangoni A, Anderson CS, Cowley D, Dowden JS, Golledge J, et al. Guideline for the diagnosis and management of hypertension in adults—2016. mortality. 2016;3:4. 95. Lawler JE, Barker GF, Hubbard JW, Schaub RG. Effects of stress on blood pressure and cardiac pathology in rats with borderline hypertension. Hypertension. 1981;3(4):496-505. 96. Kelly JJ, Mangos G, Williamson PM, Whitworth JA. Cortisol and hypertension. Clin Exp Pharmacol Physiol Suppl. 1998;25:S51-6. 97. Schnall PL, Schwartz JE, Landsbergis PA, Warren K, Pickering TG. Relation between job strain, alcohol, and ambulatory blood pressure. Hypertension. 1992;19(5):488-94.

40 | P a g e

98. Vrijkotte TGM, van Doornen LJP, de Geus EJC. Effects of Work Stress on Ambulatory Blood Pressure, Heart Rate, and Heart Rate Variability. Hypertension. 2000;35(4):880-6. 99. Schnall PL, Schwartz JE, Landsbergis PA, Warren K, Pickering TG. A longitudinal study of job strain and ambulatory blood pressure: results from a three-year follow-up. Psychosom Med. 1998;60(6):697-706. 100. Ogedegbe G, Pickering T. Principles and techniques of blood pressure measurement. Cardiol Clin. 2010;28(4):571-86. 101. Forouzanfar M, Dajani HR, Groza VZ, Bolic M, Rajan S, Batkin I. Oscillometric Blood Pressure Estimation: Past, Present, and Future. IEEE Rev Biomed Eng. 2015;8:44-63. 102. Babbs CF. Oscillometric measurement of systolic and diastolic blood pressures validated in a physiologic mathematical model. Biomed Eng Online. 2012;11(1):56. 103. Borow KM, Newburger JW. Noninvasive estimation of central aortic pressure using the oscillometric method for analyzing systemic artery pulsatile blood flow: comparative study of indirect systolic, diastolic, and mean brachial artery pressure with simultaneous direct ascending aortic pressure measurements. Am Heart J. 1982;103(5):879-86. 104. Santucci S, Gates EM, James GD, Schussel YR, Steiner D, Pickering TG. A Comparison of Two Ambulatory Blood Pressure Monitors, the Del Mar Avionics Pressurometer IV and the Spacelabs 90202. Am J Hypertens. 1989;2(10):797-9. 105. Qardio. QardioArm Blood Pressure Monitor: Qardio; 2018 [cited 2018 April 10]. 106. Apple. Nokia BPM+ Blood Pressure Monitor: Apple, Inc.; 2018 [cited 2018 April 7]. Available from: https://www.apple.com/au/shop/product/HLBA2ZM/A/nokia-bpm-blood-pressure-monitor. 107. Chahine MN, Topouchian J, Zelveian P, Hakobyan Z, Melkonyan A, Azaki A, et al. Validation of BP devices QardioArm(®) in the general population and Omron M6 Comfort(® )in type II diabetic patients according to the European Society of Hypertension International Protocol (ESH-IP). Medical Devices (Auckland, NZ). 2018;11:11-20. 108. Topouchian J ZP, Hakobyan Z, Melkonyan A, Asmar R. Validation of the QARDIO QARDIOARM upper arm blood pressure monitor, in oscillometry mode, for self measurement in persons fulfilling the population as described in this paper, according to the European Society of Hypertension International Protocol revision. dablEducational Trust. 2016. 109. Hjalmarson Å, Gilpin EA, Kjekshus J, Schieman G, Nicod P, Henning H, et al. Influence of heart rate on mortality after acute myocardial infarction. The American journal of cardiology. 1990;65(9):547-53. 110. Mason JW, Ramseth DJ, Chanter DO, Moon TE, Goodman DB, Mendzelevski B. Electrocardiographic reference ranges derived from 79,743 ambulatory subjects. J Electrocardiol. 2007;40(3):228-34. e8. 111. Evans O, Steptoe A. Social support at work, heart rate, and cortisol: a self-monitoring study. J Occup Health Psychol. 2001;6(4):361. 112. Malik M. Heart rate variability. Curr Opin Cardiol. 1998;13(1):36-44. 113. van Ravenswaaij-Arts CM, Kollee LA, Hopman JC, Stoelinga GB, van Geijn HP. Heart rate variability. Ann Intern Med. 1993;118(6):436-47. 114. Jorna P. Heart rate and workload variations in actual and simulated flight. Ergonomics. 1993;36(9):1043-54. 115. Collet C, Averty P, Dittmar A. Autonomic nervous system and subjective ratings of strain in air-traffic control. Appl Ergon. 2009;40(1):23-32. 116. Edwards S. The Heart Rate Monitor Book: Polar CIC; 1993. 117. Goldberger AL, Goldberger ZD, Shvilkin A. Clinical Electrocardiography: A Simplified Approach E-Book: Elsevier Health Sciences; 2017. 118. Zhong Y, Jan KM, Ju KH, Chon KH. Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability. Am J Physiol Heart Circ Physiol. 2006;291(3):H1475-83. 119. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007;28(3):R1-39.

41 | P a g e

120. Spierer DK, Rosen Z, Litman LL, Fujii K. Validation of photoplethysmography as a method to detect heart rate during rest and exercise. J Med Eng Technol. 2015;39(5):264-71. 121. Claes J, Buys R, Avila A, Finlay D, Kennedy A, Guldenring D, et al. Validity of heart rate measurements by the Garmin Forerunner 225 at different walking intensities. J Med Eng Technol. 2017;41(6):480-5. 122. Jo E, Lewis K, Directo D, Kim MJ, Dolezal BA. Validation of biofeedback wearables for photoplethysmographic heart rate tracking. J Sports Sci Med. 2016;15(3):540. 123. Lee CM, Gorelick M. Validity of the Smarthealth Watch to Measure Heart Rate During Rest and Exercise. Measurement in Physical Education and Exercise Science. 2011;15(1):18-25. 124. Fallow BA, Tarumi T, Tanaka H. Influence of skin type and wavelength on light wave reflectance. J Clin Monit Comput. 2013;27(3):313-7. 125. Hwang S, Seo J, Jebelli H, Lee S. Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker. Automation in Construction. 2016;71:372-81. 126. Tamura T, Huang M, Togawa T. Body Temperature, Heat Flow, and Evaporation. In: Tamura T, Chen W, editors. Seamless Healthcare Monitoring: Advancements in Wearable, Attachable, and Invisible Devices. Cham: Springer International Publishing; 2018. p. 281-307. 127. Vinkers CH, Penning R, Hellhammer J, Verster JC, Klaessens JH, Olivier B, et al. The effect of stress on core and peripheral body temperature in humans. Stress. 2013;16(5):520-30. 128. Briese E. Emotional hyperthermia and performance in humans. Physiol Behav. 1995;58(3):615-8. 129. Renbourn ET. Body temperature and pulse rate in boys and young men prior to sporting contests. A study of emotional hyperthermia: With a review of the literature. J Psychosom Res. 1960;4(3):149-75. 130. Marazziti D, Di Muro A, Castrogiovanni P. Psychological stress and body temperature changes in humans. Physiol Behav. 1992;52(2):393-5. 131. Vergara RC, Moënne-Loccoz C, Maldonado PE. Cold-Blooded Attention: Finger Temperature Predicts Attentional Performance. Front Hum Neurosci. 2017;11:454. 132. Kasper DL, Jameson JL, Hauser SL, Loscalzo J, Fauci AS, Longo DL. Harrison's Principles of Internal Medicine 19/E (Vol.1 & Vol.2): McGraw-Hill Education; 2015. 133. Kocoglu H, Goksu S, Isik M, Akturk Z, Bayazit YA. Infrared tympanic thermometer can accurately measure the body temperature in children in an emergency room setting. Int J Pediatr Otorhinolaryngol. 2002;65(1):39-43. 134. Gasim GI, Musa IR, Abdien MT, Adam I. Accuracy of tympanic temperature measurement using an infrared tympanic membrane thermometer. BMC Res Notes. 2013;6:194-. 135. Amoateng-Adjepong Y, Del Mundo J, Manthous CA. Accuracy of an infrared tympanic thermometer. Chest. 1999;115(4):1002-5. 136. Braun GmbH. ThermoScan® 7 Age Precision® – IRT6520: Braun GmbH; 2018 [cited 2018 April 24]. Available from: https://www.brauntherms.com/thermometer/thermoscan-7-ear-thermometer-irt6520/. 137. Nurofen(R). FeverSmartTM Temperature Monitor. Reckitt Benckiser; 2017. 138. Martin D, Powers S, Cicale M, Collop N, Huang D, Criswell D. Validity of pulse oximetry during exercise in elite endurance athletes. J Appl Physiol. 1992;72(2):455-8. 139. Aeschliman SD, Blue MS, Williams KB, Cobb CM, MacNeill SR. A preliminary study on oxygen saturation levels of patients during periodontal surgery with and without oral conscious sedation using diazepam. J Periodontol. 2003;74(7):1056-9. 140. Miller MT. Crime Scene Investigation Laboratory Manual: Elsevier Science; 2018. 141. Johnson AT. Respirator masks protect health but impact performance: a review. J Biol Eng. 2016;10:4. 142. FitOn. FitOn: FitOn; 2018 [cited 2018 April 20]. Available from: https://fitonwatch.com. 143. Finkenzeller K. RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification: Wiley; 2003. 144. Want R. An introduction to RFID technology. IEEE Pervasive Computing. 2006;5(1):25-33.

42 | P a g e

145. Williams SR, Taylor MK, Mehta A, Jeffrey I. RFID Technology in Forensic Evidence Management: An Assessment of Barriers, Benefits, and Costs. 2014. 146. Suh WS, Yoon EJ, Piramuthu S. RFID-Based Attack Scenarios in Retailing, Healthcare and Sports. Journal of Information Privacy and Security. 2013;9(3):4-17. 147. Pique S. The Truth About RFID Read Rates. RFID Journal. 2012. 148. Dellinger D. How a pair of microchips could transform football into an intricate dance of data: The Daily Dot; 2017 [cited 2018 April 7]. 149. Tilley A. How RFID Chips Are Changing The NFL: Forbes; 2016 [cited 2018 April 10]. 150. Waterhouse P, Stevens JK, August J, Olson JL. Evidence tracking. Google Patents; 2008. 151. Wessel R. Dutch forensic institute uses RFID to control crime evidence. RFID Journal. 2008. 152. Swedberg C. Travis County fire marshall uses RFID to manage evidence. RFID Journal. 2008. 153. Krüger G, Springer R, Lechner W. Global navigation satellite systems (GNSS). Computers and electronics in agriculture. 1994;11(1):3-21. 154. Kaplan E, Hegarty C. Understanding GPS: principles and applications: Artech house; 2005. 155. National Coordination Office for Space-Based Positioning N, and Timing,. GPS: Space Segment 2018 [cited 2018 April 10]. Available from: https://www.gps.gov/systems/gps/space/. 156. US Department of Defence. Global positioning system standard positioning service performance standard. Assistant secretary of defense for command, control, communications, and intelligence. 2008. 157. PC Mag. Definition of: geotracking: The Computer Language Company; 2018 [cited 2018 April 18]. Available from: https://www.pcmag.com/encyclopedia/term/62908/geotracking. 158. Russian Space Systems. GLONASS Constellation. Moscow: Russian Space Systems.; 2011 [updated 2018; cited 2018 April 2]. [Figure]. 159. Septentrio Inc. GNSS Receivers: Septentrio Inc,; 2015 [cited 2018 April 26]. Available from: https://www.septentrio.com/products/gnss-receivers. 160. Zandbergen PA. Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning. Transactions in GIS. 2009;13(s1):5-25. 161. Miller KM. A review of GLONASS2000. 15-21 p. 162. Guerrier S. Improving accuracy with multiple sensors: Study of redundant MEMS-IMU/GPS configurations2009. 163. Department of Transport FAA. GLOBAL POSITIONING SYSTEM WIDE AREA AUGMENTATION SYSTEM (WAAS)

PERFORMANCE STANDARD. GPS.gov; 2008. 164. Sarkar S, Bose A. Contribution of GLONASS in Multi-GNSS Solution Accuracy2015. 13 p. 165. Truong DM, Ta TH, editors. Development of real multi-GNSS positioning solutions and performance analyses. Advanced Technologies for Communications (ATC), 2013 International Conference on; 2013: IEEE. 166. Johnson L, Diggelen Fv, editors. Advantages of a combined GPS+GLONASS precision sensor for machine control applications in open pit mining. IEEE 1998 Position Location and Navigation Symposium (Cat. No.98CH36153); 1998 20-23 Apr 1998. 167. Rampinini E, Alberti G, Fiorenza M, Riggio M, Sassi R, Borges T, et al. Accuracy of GPS devices for measuring high-intensity running in field-based team sports. Int J Sports Med. 2015;36(1):49-53. 168. Hirsch JA, Winters M, Clarke P, McKay H. Generating GPS activity spaces that shed light upon the mobility habits of older adults: a descriptive analysis. International journal of health geographics. 2014;13(1):51. 169. Bishop L. The challenges of GPS and sex offender management. Fed Probation. 2010;74:33. 170. Rosenberg K. Location Surveillance by GPS: Balancing an Employer's Business Interest with Employee Privacy. Wash JL Tech & Arts. 2010;6:143. 171. Baber C, Smith P, Cross J, Hunter JE, McMaster R. Crime scene investigation as distributed cognition. Pragmatics & Cognition. 2006;14(2):357-85.

43 | P a g e

172. Landgate. Geodetic Survey Marks Web Mapping Service [Internet]: Western Australian Government; 2016 [cited 2018 March 13]. Available from: https://catalogue.data.wa.gov.au/dataset/geodetic-survey-marks-point/resource/e568c43f-fa58-3479-9c3d-f7ecf1fce403. 173. BOM. Monthly Climate Summary for Perth [Internet]: Australian Government; 2017 [cited 2018 March 5]. Available from: http://www.bom.gov.au/climate/current/month/wa/archive/201704.perth.shtml. 174. Suping Z, Guanglin M, Yanwen W, Ji L. Study of the relationships between weather conditions and the marathon race, and of meteorotropic effects on distance runners. Int J Biometeorol. 1992;36(2):63-8. 175. Gaffen DJ, Ross RJ. Increased summertime heat stress in the US. Nature. 1998;396(6711):529. 176. Rosa RR. Extended workshifts and excessive fatigue. J Sleep Res. 1995;4:51-6.

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Part Two:

Using Wearable Technology on Crime Scene Investigators to

Monitor Physiological Indicators of Stress

and

Using Wearable Technology on Crime Scene Investigators to

Monitor Position in a Crime Scene

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Using Wearable Technology on Crime Scene Investigators to Monitor Physiological

Indicators of Stress

Elizabeth Morahan1, Brendan Chapman1

1 Medical and Molecular Sciences, School of Veterinary and Life Sciences, Murdoch University. 90 South Street, Murdoch, WA, 6150. Stress is ingrained in the modern workplace. Poorly managed stress can lead to a decline in performance, as well negatively impact the health of employees. Therefore, it is essential that stress in the workplace be adequately managed. Crime scene investigation, in particular, has been shown to be a particularly stressful occupation, and if not conducted properly, can have far-reaching consequences, including the obstruction of justice. Modern consumers have embraced wearable activity monitors, with sales expected to total 340 million units this year, worldwide. Devices can track a user’s location, as well as physiological measures such as heart rate. These functions have been used in various settings, including in elite sports, workplaces and clinical environments, and also have the potential to be applied to crime scene investigation through monitoring physiological metrics which are indicative of stress. Crime scene teams may be monitored and managed to ensure that investigators avoid fatigue and the subsequent decline in cognitive function.

This study aimed to assess the ability of wearable activity monitors in measuring the physiological

indicators of stress/fatigue in crime scene investigators. To achieve this, we conducted a simulated, two-

day clandestine grave body recovery exercise with university Master students, while wearing an activity

monitor, the Garmin fēnix® 5. Three other physiological measures (body temperature, blood pressure, and

saturated oxygen levels) were also measured using other wearable devices. Indicators of stress were visible

blood pressure and heart rate. The method used to measure body temperature was found to be unreliable.

Saturated oxygen levels appeared to decline when a mask was worn in one participant. While the findings

are insightful, further research is needed to validate these wearable devices for the early detection stress

in crime scene investigators.

Key Words: stress, fatigue, forensic, crime scene investigator, wearable technology, physiology.

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1 TABLE OF CONTENTS

2 List of Figures .......................................................................................................................................................... 48

3 List of Tables............................................................................................................................................................ 49

4 Introduction ............................................................................................................................................................ 50

5 Methods .................................................................................................................................................................. 52

5.1 Heart Rate ....................................................................................................................................................... 53

5.1.1 Heart Rate Graphs ................................................................................................................................... 53

5.1.2 Heart Rate Variability Analysis ................................................................................................................ 54

5.2 Blood Pressure ................................................................................................................................................ 55

5.3 Saturated Oxygen Levels ................................................................................................................................. 56

5.4 Self-Reported Surveys and Body Temperature Data ...................................................................................... 56

6 Results ..................................................................................................................................................................... 57

6.1 Heart Rate ....................................................................................................................................................... 57

6.1.1 Heart Rate Graphs ................................................................................................................................... 57

6.1.2 Heart Rate Variability Analysis ................................................................................................................ 60

6.2 Blood Pressure ................................................................................................................................................ 62

6.3 Saturated Oxygen ............................................................................................................................................ 64

6.4 Self-Reported Surveys and Temperature Monitoring..................................................................................... 66

7 Conclusion ............................................................................................................................................................... 66

8 References .............................................................................................................................................................. 68

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2 LIST OF FIGURES

Figure 1. Heart rate of Participant 1 on Day one (top) and day two (bottom) ............................................................... 57

Figure 2. Heart rate data from Participant 2 on day 1 (top) and day 2 (bottom) ........................................................... 58

Figure 3. Heart rate Zones and time spent in various stress states for Participant 2, on day one (top) and day two

(bottom)1 ......................................................................................................................................................................... 60

Figure 4. Heart rate zones and time spent in various stress states for Participant 1, on day one (top) and day two

(bottom)1 ......................................................................................................................................................................... 60

Figure 5. Systole measurements for Participant 2 over Day 1 and 2. ............................................................................. 62

Figure 6. Saturated O2 levels according to the presence of a face mask, for participant 1 over day 1 (top) and day 2

(bottom) .......................................................................................................................................................................... 64

Figure 7. Saturated O2 levels according to the presence of a face mask, for participant 2 over day 1 (top) and day 2

(bottom) .......................................................................................................................................................................... 64

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3 LIST OF TABLES

Table 1. Kubios © HRV parameters for calculation of heart rate zones and stress zones38 ........................................... 55

Table 2. Percentage of heart rate measurements above the resting baseline, for each participant, for each day ....... 59

Table 3. Percentage of time participants spent in the high-stress zone, over the course of the day, relative to baseline

percentage. ..................................................................................................................................................................... 60

Table 4. Percentage of systolic measurements above baseline for Participant 1 and 2 over both days ....................... 63

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4 INTRODUCTION

Stress is a key component of the modern workplace2. High levels of stress in the community is known to

have an enormous economic impact, through decreased productivity and high use of health care services3.

When a person becomes stressed, fatigue and increased errors often result4.

Crime scene investigators have a higher predisposition to fatigue and stress. This is due to several factors,

including long working hours, less than ideal working conditions and traumatic scenes5, 6. Stress associated

with crime scene investigation is a notably under-researched subject area, with little preventative

measures in place to combat it4, 7-9. One study showed that stress could potentially affect the collection of

evidence10. This is a severe problem, as evidence collected in the process of crime scene investigation is

pivotal in the court process11. Modern-day jurors and prosecutors expect forensic evidence in the court

process, and high standards are expected of all evidence collected by crime scene investigators12. Any

errors in the collection of evidence could therefore then have an adverse downstream effect, and could

ultimately culminate in a miscarriage of justice13.

Stress management in the workplace is an emerging topic which has many proposed solutions. Stress can

be monitored in one of two ways, surveys and physiological metrics. Self-reporting surveys are inherently

associated with bias, however. Physiological metrics can overcome this bias, to detect stress as it is

occuring. Physiological measures that can be used to indicate stress include blood pressure, heart rate,

body temperature.

Stress is known to influence blood pressure14. This is because cortisol, a hormone produced when an

individual is placed in a stressful environment, is a crucial regulator of blood pressure15. Studies focussing

on detecting stress in the workplace have found that in stressful work conditions, participants recorded a

significantly higher systolic blood pressure16-18. Heart rate variability analysis is a widely accepted method

of evaluating the nervous system19. It assumes that the RR interval in heart rate, which is the interval

between R peaks in the ECG, varies according to the autonomic nervous system, and is thus directly

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impacted by stress19. Body temperature is also affected by stress, although this has been found to differ

according to where measurement is conducted20-22. It is therefore theoretically possible to use these

physiological metrics to detect stress.

In order to use these indicators to detect stress in a workplace setting, measuring devices must be

comfortable, practical and minimally invasive, while not compromising on accuracy and reliability. Modern

wearable devices, by companies such as Apple® Garmin® and Fitbit®, have risen in popularity in recent

years23. They are equipped with many of the necessary tools for measurement of stress-related changes in

the body, such as heart rate, exist in practical, comfortable to wear formats such as smartwatches24 and

bands25, and have been validated for use in various setting26-29.

This study aimed to determine if stress can be quantified in crime scene investigators using wearable

devices. A secondary aim to assess the effect of face masks on saturated oxygen levels was also

implemented.

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5 METHODS

This study followed two healthy Forensic Masters students, a male and a female in their early twenties, as

they conducted a clandestine body recovery exercise. Students were formed into teams, with each

member adopting one of the following roles: Crime Scene Manager, Photographer, Mapper, Exhibits

Officer and Logistics Personnel. The male and female tested were both assigned as Crime Scene Managers

of their respective scenes. In these roles, students were tasked with finding, excavating and processing a

porcine carcass buried in soil on a semi-rural property over two days, in a manner consistent with a real-life

investigation.

This exercise was conducive to fatigue and stress, due to numerous factors. Firstly, as the task took place

during early autumn, temperatures were sufficiently high enough30 that prolonged exposure would

exacerbate fatigue31 and heat stress32. Secondly, the working hours were long, from about 8 AM until 5

PM, which, as demonstrated above, is conducive to fatigue33. Additionally, participants camped overnight,

as officers investigating rural scenes in Australia would do.Thirdly, the task was cognitively demanding,

with students under time pressure to produce accurate results through a role-playing supervisor,

mimicking the pressure placed on forensic personnel by real-life detectives.

Over both days, the participants wore the following monitors: a Garmin fēnix® 5, a smartwatch, with

GPS/GLONASS capabilities; Qardioarm, a wireless blood pressure cuff; Nurofen® Feversmart, a patch the

measures body temperature, worn in the armpit and a Nonin GO2 Achieve, a finger pulse oximeter for

measuring saturated oxygen levels.

The Garmin fēnix® 5 and the Nurofen® FeversmartTM were worn for the duration of the exercise, while

measurements were obtained from the Qardioarm and the Nonin GO2 on an hourly basis. Two self-

reported surveys were used, the Perceived Stress Survey34 and Chalder Fatigue scale35 were conducted at

the start and conclusion of the day, as well as during lunch, with the aim of noting the correlation between

changes in measurements and self-reported instances of fatigue and stress.

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Baseline measurements were taken for heart rate, blood pressure and saturated O2. Participants wore the

Garmin fēnix® 5 for five days during a typical week, activating the ‘track me’ function daily with the same

settings as during the body recovery exercise. Blood pressure was taken with the QardioArmTM once hourly

for 5 hours on an average day. The Nonin Achieve® was used to take a saturated O2 reading at the

beginning of the body recovery exercise, while the participants were not wearing a mask.

5.1 HEART RATE The heart rate data obtained from the Garmin fēnix® 5 was analysed in two ways: through graphing raw

heart rate data, and by conducting heart rate variability analysis.

5.1.1 Heart Rate Graphs

Data from the Garmin fēnix® 5 data was obtained from the Garmin ConnectTM website, in the form of a .tcx

file per day, for each participant.

Heart rate data was then extracted using the open source cycling analytics application Golden Cheetah36,

using standard settings, and exported as a .csv file. No change was made to the file itself, other than to the

file extension.

All unrelated data contained in the file was deleted, keeping only the heart rate in BPM and the

corresponding time of day, to the second at which it was taken. Heart rate readings of below 50 were

classed as errors in device measurement and were removed.

The heart rate was graphed over time using Microsoft Excel, on scatter graph with lines, with two graphs

constructed for each participant, one for each day. Heart rate line was coloured blue for day one, while day

two was coloured green. The mean was taken of the participants’ five average daily heart rates and plotted

on the relevant graphs as an orange line. The number of times a participant’s heart rate exceeded their

baseline measurement was determined using the COUNTIF function in Excel. Notable heart rate spikes

were compared to the handwritten activity log completed by participants contemporaneously to see if a

causative event could be attributed.

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5.1.2 Heart Rate Variability Analysis

RR intervals are used to calculate heart rate variability and were calculated from the .csv described above.

The following formula was used:

The resultant RR intervals were ordered in an Excel spreadsheet next to the corresponding time of capture

in seconds. This was then converted into a .txt file for heart rate variability analysis, and imported into

Kubios © HRV Standard version 3.1.0, which is software designed to calculate heart rate variability37.

Import settings were the subject’s age, sex, weight, height and maximum heart rate, all of which affect the

calculation of heart rate zones.

This software measures stress using a Stress index parameter as the square root of Baevsky’s stress

index19. Baevsky’s stress index operates under the formula:

where AMo is the height of a normalised RR interval histogram, Mo is the most frequent RR interval, and

MxDMn is the difference between the longest and shortest RR intervals38.

Heart rate zones were also calculated, as a percentage of each participant’s maximum heart rate.

The parameters for the calculation of stress zones, as well as the heart rate zones, are given below (table

1).

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Table 1. Kubios © HRV parameters for calculation of heart rate zones and stress zones38

HR ZONES STRESS ZONES

(of HRmax) (√SI) (Baevsky’s SI)

MAXIMUM 90-100% VERY HIGH ≥30 (≥900)

HARD 80–90% HIGH 22.4-30 (500-900)

MODERATE 70-80% ELEVATED 12.2-22.4 (150-500)

LIGHT 60-70% NORMAL 7.1-12.2 (50-150)

VERY LIGHT 50-60% LOW <7.1 (<5)

INACTIVE <50%

Results were obtained from Kubios © HRV in pdf format.

Analysis of the baseline measurements was also conducted in Kubios © HRV, using the method described

above. Comparison between the mean times spent in the high stress zone in a normal week and the

activity were made to evaluate whether there was a notable difference.

5.2 BLOOD PRESSURE Blood pressure data was recorded in the QardioArm smartphone application and manually entered into an

excel spreadsheet for graphical analysis. Diastole measurements were disregarded, as an increase in

systolic blood pressure is sufficient to show stress16-18. All participants individual measurements were

plotted over time on an X Y scatter graph, with Day one represented as circles and day two as triangles.

The graph was shaded with bands based on the classifications of clinic systolic blood pressure as given by

the National Heart Foundation of Australia39. Systolic measurements of <130mmHg are optimal, and

shaded dark green; High normal is between 130 – 139mmHg and shaded light green; Grade 1 Hypertension

is between 140-159mmHg and shaded yellow; Grade 2 Hypertension is between 160-179mmHg and

shaded orange, and Grade 3 hypertension is above 180mmHg and shaded red.

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A baseline was also obtained by measuring participants hourly over the course of a day, for a total of five

measurements. The mean of these measurements for each participant was plotted as a horizontal red line

on each graph.

5.3 SATURATED OXYGEN LEVELS Saturated Oxygen levels were manually inserted into an Excel spreadsheet. Readings were sorted by day

and by whether a mask had been worn in the hour preceding measurement, and plotted against the time,

with each graph consisting of a different participant on a different day. Measurements for which a mask

was worn was represented by a circle, while a triangle meant no mask was worn. Horizontal bands

displaying the recommended saturated oxygen levels formed the background of each graph. Normal

saturated O2, which is 95-100%, was shaded green; low-normal, given as 90-94%, was shaded yellow; and

low below 90% was shaded red. The baseline measurement was plotted in the form of a red line.

5.4 SELF-REPORTED SURVEYS AND BODY TEMPERATURE DATA The self-reported fatigue and stress surveys were entered into an Excel spreadsheet. Temperature data

was obtained from the FeversmartTM app and manually entered into an Excel spreadsheet. Upon

examination of the data presented, no further analysis was performed.

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Figure 1. Heart rate of Participant 1 on Day one (top) and day two (bottom)

6 RESULTS

6.1 HEART RATE

6.1.1 Heart Rate Graphs

Raw heart rate data produced the following graphs (Figure 1 and Figure 2).

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Figure 1. Heart rate data from Participant 2 on day 1 (top) and day 2 (bottom)

The blue lines (day one) and the green lines (day two) represent the heart rate as recorded by the Garmin

fēnix® 5 at approximately one-second intervals.

No trends were observed in relation to the direction of the heart rate over the course of the days.

Possible device errors are visible in the results of all participants, notably a reading of 55 and 60 BPM for

the participant at around 1300hrs and 1530hrs respectively on day one (figure 2). It is impossible to know

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definitively if these are indeed misreadings, however some inaccuracy in heart rate optical sensors is

expected, and commonly attributed to movement of the sensor29, 40-42.

Participant 1 recorded their highest heart rate reading at 141 bpm, just prior to the recommencement of

the activity on day 2. Other heart rate spikes could not be attributed, even tenuously, to any given event

due to significant time gaps in their contemporaneous notes. In participant 2 heart rate spikes frequently

correlated with the packaging of crucial evidence, as found in the notes. However, it is impossible to give

definitive causes for the most of this due to the pulsatile nature of heart rate. Participant 2, however,

experienced a heart rate spike at 1430 on day one which can be definitively linked with the arrival of the

teaching supervisor with information related to the activity, as it was witnessed by the researcher.

Additionally, participant 2 took lunch breaks from 1337 until 1404 on day one and 1220 until 1257 on day

two, which are characterised by a period of low heart rate readings (figure 2), most notably in day one. In

participant 1 there were no discernable periods of low heart rate that correlated with the noted lunch

breaks.

The measured heart rate of the participants was found to be frequently above the average baseline

measurement for each participant (Table 1). The only exception was Participant 1 on day 1. This suggests

that the participants experienced stress throughout the activity.

Table 2. Percentage of heart rate measurements above the resting baseline, for each participant, for each day

Increased heart rate is indicative of stress16. However, heart rate is highly variable, and spikes are frequent

throughout the day. It is difficult to identify precisely what activity is leading to the specific spikes in heart

rate, as they are frequently caused by commonplace activities such as walking, running, etc. Therefore,

unless conclusive evidence as to the cause of the spike is provided, it is not advisable to make broad

DAY ONE DAY TWO

PARTICIPANT 1 47% 76.36%

PARTICIPANT 2 89.77

78.8%

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Figure 2. Heart rate Zones and time spent in various stress states for Participant 2, on day one (top) and day two (bottom)1

Figure 3. Heart rate zones and time spent in various stress states for Participant 1, on day one (top) and day two (bottom)1

conclusions. In future studies, changes in heart rate could potentially be linked to specific activities by

fitting participants with body cameras.

6.1.2 Heart Rate Variability Analysis

Kubios ® HRV software generated the following profiles for each participant 1 (figure 3) and participant 2

(figure 4).

All participants only spent time in the elevated, high and very high-stress zones (figures 4 and 5). This

indicates a stressful environment. Participant 2 appeared to undertake more physical exertion than

participant 1, due to the percentage of time spent in the maximum heart rate zone.

The percentage of time each participant spent in the high-stress zone is given below (Table 2).

Table 3. Percentage of time participants spent in the high-stress zone, over the course of the day, relative to baseline percentage.

DAY ONE DAY TWO BASELINE

PARTICIPANT 1 59.42% 77.68% 40.46%

PARTICIPANT 2 92.8% 89.72% 59.88%

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Both participants spent over 50% of their day in a high-stress state (table 2), despite the relatively limited

exertion rate as determined by the heart rate zones (figures 4 and 5). Participant 1 spent less time in the

highest stress zone, compared to Participant 2. Additionally, participant 1 had a higher percentage on day

two than day one, while the reverse was true of participant 2. This highlights the different ways in which

individuals experience, and are affected by, the similar stressful activities and could be used as a tool in

managing personnel in the field.

Kubios analysis shows all participants remained in a very high-stress state over the course of the exercise.

It should be noted, however that there are some limitations to measurements obtained through

photoplethysmography. The sensor is highly susceptible to movement artefacts and is reliant upon an

algorithm to correct this29, 41. Skin type may also affect the measurement of heart rate43. Heart rate

variability analysis requires sensitive sampling, which may not be provided by such a technique44. The

technique used, therefore, requires further research to determine the accuracy of data obtained, as well as

the usefulness of the data. The orthodox method, an electrocardiogram and a chest strap is cumbersome

and is too complicated for continuous, everyday monitoring, but provides accurate data.45

Analysis undertaken by the software program was thorough and complex, but due to time constraints, the

majority of the data obtained in the obtained analysis has not been examined. Future research may choose

to focus on the examination of other heart rate variability analysis tools provided by the program, such as

Ponicaré plots. A comparison of these results to other heart rate variability software which would use

different algorithms in their calculations is also recommended.

Overall heart rate data obtained from the Garmin Fenix 5 can be used to indicate stress. However, this

research is in its infancy, and the data obtained was complicated to extract, due to the proprietary file

formats used by various device manufacturers. A more accessible mechanism for extracting heart rate

must be developed before this can be applied in a workplace environment.

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Figure 5. Systolic measurements for Participant 1 over Day one and Day Two

Figure 4. Systole measurements for Participant 2 over Day 1 and 2.

6.2 BLOOD PRESSURE Graphs obtained from systole measurements for participant 1 and participant 2 are shown in Figures 5 and 6.

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Blood pressure did not remain constant for either participant. Both participants showed a downward trend

for day one, and an upward trend for day two. This could be interpreted as an indicator of stress, although

it is common for blood pressure to fluctuate throughout the day, in response to natural increases in heart

rate.

Definitive spikes occurred for both participants, occurring on notably on day one, at around 0900hrs for

participant 1 and 1000hrs for participant 2. At these time, as recorded in their contemporaneous notes,

participant 1 was receiving a briefing from the teaching advisor in the role of a detective, while participant

2’s crime scene team officially entered their scene. Both can be viewed as stressful, however, the

correlation between the spikes and the event is tenuous and may be unrelated.

Neither participant entered into dangerously hypertensive regions, although participant 1 recorded Grade

1 hypertension results on two occasions, both in the morning on day 1. This could be associated with the

stress of attempting a new task for the first time. These results were compared to the crime scene notes in

an attempt to identify a cause. However, as previously stated, these links are tenuous, and the cannot be

conclusively attributed to any cause.

The percentage of systolic measurements above their relevant baselines, for both participants, are given

below (Table 3).

Table 4. Percentage of systolic measurements above baseline for Participant 1 and 2 over both days

DAY ONE DAY TWO

PARTICIPANT 1 90% 62.5%

PARTICIPANT 2 78% 44%

Both participants were consistently above their baseline systole (Table 2), which could potentially indicate

that the participant experienced a heightened stress state16, 17. However, the number of measurements

isrelatively small, and this is an average over the whole day. In a workplace environment, stress detection

must be linked to a specific period of time in order to facilitate the successful management of stress.

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Figure 5. Saturated O2 levels according to the presence of a face mask, for participant 1 over day 1 (top) and day 2 (bottom)

Figure 6. Saturated O2 levels according to the presence of a face mask, for participant 2 over day 1 (top) and day 2 (bottom)

Despite the wearable nature of the blood pressure monitor, some of the disadvantages of traditional

methods still apply. For example, measurement must be taken each time manually, using the QardioArm

application. This makes incorporation into a workplace monitoring scheme difficult, as measurements in

such a scheme must be seamless and require minimal effort on behalf of the monitored employee.

However, technology is evolving rapidly, and in the future, there may be a blood pressure device that

operates with very little interference to the user.

Overall, it was possible to detect stress in the blood pressure data. However, due to limited analysis and

small sample size, conclusions are relatively limited.

6.3 SATURATED OXYGEN Graphs obtained for saturated oxygen levels are shown in figure 7 and 8 below.

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Participant 1 recorded saturated oxygen levels in the low-normal values four times throughout the course

of the exercise, all occurring when a mask had been worn for a minimum of one hour. Participant 2 stayed

within the normal range for the duration of the exercise. However 89% of the readings recorded were

below their baseline measurement. Unlike participant 2 however, mask wear seemed to have had minimal

impact on their saturated oxygen levels, as low levels were detected regardless of the presence of a mask.

This could be for a variety of reasons. Firstly, the mask may not have been worn correctly when it was

present. Secondly, the baseline recorded may be an anomaly, therefore skewing comparison. Thirdly, the

adverse effects of mask wear may actually continue after it is removed, thus influencing saturated oxygen

levels of beyond the time period in which it was worn46. Lastly, there may be other unknown physiological

factors impacting the participant’s saturated oxygen levels. Issues such as incorrect mask use and the

possibility of an outlier as a baseline could be resolved in future studies through a larger sample size, as

well as focusing on when and how the masks are worn.

Participant 1 recorded a low saturated oxygen reading of 93% on day two. While this is not a cause for

immediate medical attention, it is not ideal, as lack of oxygen can have negative consequences for

cognition47. As an example, working with a saturated oxygen level of 93% can be equated to working on at

an altitude of around 3000m47. Full cognitive ability is required when working at on crime scene, in order

to avoid miscarriages of justice12.

Saturated Oxygen levels appeared lower for participant 2 on day two. Exhumation of the decomposed pig

occurred on day two, so it is possible that when the participant using a facemask, they wore it tightly and

with more reliant on the mask during this time.

Saturated O2, while not directly related to stress and fatigue, is vitally important, as oxygen flow is

significant for cognitive function46. The low results obtained from participant 1 warrant further

investigation. This study acknowledges several limitations, such as small sample size and unregimented

wearing of the mask, which must be eliminated in order to obtain conclusive results. A trial in a more

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controlled environment, where a large sample of participants wears the face mask continually would

explore this potential link thoroughly.

6.4 SELF-REPORTED SURVEYS AND TEMPERATURE MONITORING It was found that the self-reported surveys used in this study could not be used due to their lack of

specificity. In a future study, self-reported surveys should be taken hourly, in order to obtain more valuable

data.

Data obtained from the temperature monitor was deemed unreliable, as the adhesive patch did not have

consistent contact with either participant for the entirety of the testing period for either participant. For

future study, a temperature sensor in a smartwatch or wristband may be more practical.

6.5 CONCLUSION

Successful monitoring of stress at a crime scene not only looks after the health and safety of investigators

but may also improve the quality of the investigation, as stress has a demonstrated adverse effect on

cognition. A smartwatch, portable blood pressure monitor and pulse oximeter were used to detect stress

in participants with success. Ultimately, this research shows stress can be visualised using wearable devices

on crime scene personnel. However, linking the physiological data to the specific stressful occurrence is

more challenging, as self-reported surveys and crime scene notes were not specific enough to make

conclusive assessments. Additionally, the small sample size and limited time frame for complex statistical

analysis limits the scope of this study.

In future, a similar study could be performed with the addition of a body camera, allowing moments of

stress to be visualised. Additional research also needs to be undertaken to facilitate the cohesive, efficient

analysis of data obtained from the wearable devices, which would allow implementation of a crime scene

investigator’s workplace.

Stress management in the workplace as a science is in its infancy. Wearable devices have shown potential

as a solution which would be minimally invasive and easy to use. The research presented here is a

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preliminary attempt at developing a method for stress detection using wearable devices, which would

allow for more natural management of crime scene investigators. Ultimately, the successful detection of

stress in this study, and the importance of the subject matter mandate that further research is required.

This study also revealed a potential link between face masks and a decrease in saturated oxygen levels,

which should be explored with a more controlled study in the future.

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7 REFERENCES

1. Kubios HRV Software. Results from analysis of RR intervals from Heart Rate Data. Kubios HRV software2018. 2. The State of Our Nation: Stress in America [press release]. American Psychological Association2017. 3. Kalia M. Assessing the economic impact of stress — The modern day hidden epidemic. Metabolism-Clinical and Experimental. 2002;51(6):49-53. 4. M. Jeanguenat A, Dror I. Human Factors Effecting Forensic Decision Making: Workplace Stress and Well-being2017. 5. Leone M, Keel R. Occupational Stress and the Crime Scene Investigator2016. 6. Vila B. Impact of long work hours on police officers and the communities they serve. Am J Ind Med. 2006;49(11):972-80. 7. Sewell JD. Identifying and mitigating workplace stress among forensic laboratory managers. Forensic Science Communications. 2000;2(2):NA>-NA>. 8. Holt TJ, Blevins KR, Foran RWS. Examination of the Conditions Affecting Forensic Scientists’ Workplace Productivity and Occupational Stress. 2016. 9. Sedighi Maman Z, Alamdar Yazdi MA, Cavuoto LA, Megahed FM. A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. Appl Ergon. 2017; forthcoming. 10. Adderley R, Smith LL, Bond JW, Smith M. Physiological Measurement of Crime Scene Investigator Stress. International Journal of Police Science & Management. 2012;14(2):166-76. 11. NFSTC. Crime Scene Investigation Guide: National Forensic Science Technology Center; 2013 [cited 2018 February 26]. Available from: https://www.nfstc.org/product/crime-scene-investigation-guide/. 12. Julian R, Kelty S, Robertson J. Get It Right the First Time: Critical Issues at the Crime Scene Special Issue: Forensic Science and Justice: From Crime Scene to Court and Beyond. Current Issues in Criminal Justice. 2012;24:25-38. 13. Ryan TG, Overlin TK, editors. Human reliability assessment: tools for law enforcement. Training, Education, and Liability Issues for Law Enforcement Scientists and Engineers; 1997: International Society for Optics and Photonics. 14. Lawler JE, Barker GF, Hubbard JW, Schaub RG. Effects of stress on blood pressure and cardiac pathology in rats with borderline hypertension. Hypertension. 1981;3(4):496-505. 15. Kelly JJ, Mangos G, Williamson PM, Whitworth JA. Cortisol and hypertension. Clin Exp Pharmacol Physiol Suppl. 1998;25:S51-6. 16. Vrijkotte TGM, van Doornen LJP, de Geus EJC. Effects of Work Stress on Ambulatory Blood Pressure, Heart Rate, and Heart Rate Variability. Hypertension. 2000;35(4):880-6. 17. Schnall PL, Schwartz JE, Landsbergis PA, Warren K, Pickering TG. Relation between job strain, alcohol, and ambulatory blood pressure. Hypertension. 1992;19(5):488-94. 18. Schnall PL, Schwartz JE, Landsbergis PA, Warren K, Pickering TG. A longitudinal study of job strain and ambulatory blood pressure: results from a three-year follow-up. Psychosom Med. 1998;60(6):697-706. 19. М. Baevsky R, G. Chernikova A. Heart rate variability analysis: physiological foundations and main methods2017. 66-76 p. 20. Briese E. Emotional hyperthermia and performance in humans. Physiol Behav. 1995;58(3):615-8. 21. Renbourn ET. Body temperature and pulse rate in boys and young men prior to sporting contests. A study of emotional hyperthermia: With a review of the literature. J Psychosom Res. 1960;4(3):149-75. 22. Marazziti D, Di Muro A, Castrogiovanni P. Psychological stress and body temperature changes in humans. Physiol Behav. 1992;52(2):393-5. 23. Wearables Market to Be Worth $25 Billion by 2019 [press release]. CCS Insight2015.

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24. Chuah SH-W, Rauschnabel PA, Krey N, Nguyen B, Ramayah T, Lade S. Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Comput Human Behav. 2016;65:276-84. 25. Fitbit Inc. Fitbit Charge Hr: Fitbit Inc,; 2018 [cited 2018 April 24]. Available from: https://www.fitbit.com/au/chargehr. 26. Topouchian J ZP, Hakobyan Z, Melkonyan A, Asmar R. Validation of the QARDIO QARDIOARM upper arm blood pressure monitor, in oscillometry mode, for self measurement in persons fulfilling the population as described in this paper, according to the European Society of Hypertension International Protocol revision. dablEducational Trust. 2016. 27. Takacs J, Pollock CL, Guenther JR, Bahar M, Napier C, Hunt MA. Validation of the Fitbit One activity monitor device during treadmill walking. J Sci Med Sport. 2014;17(5):496-500. 28. Chahine MN, Topouchian J, Zelveian P, Hakobyan Z, Melkonyan A, Azaki A, et al. Validation of BP devices QardioArm(®) in the general population and Omron M6 Comfort(® )in type II diabetic patients according to the European Society of Hypertension International Protocol (ESH-IP). Medical Devices (Auckland, NZ). 2018;11:11-20. 29. Jo E, Lewis K, Directo D, Kim MJ, Dolezal BA. Validation of biofeedback wearables for photoplethysmographic heart rate tracking. J Sports Sci Med. 2016;15(3):540. 30. BOM. Monthly Climate Summary for Perth [Internet]: Australian Government; 2017 [cited 2018 March 5]. Available from: http://www.bom.gov.au/climate/current/month/wa/archive/201704.perth.shtml. 31. Suping Z, Guanglin M, Yanwen W, Ji L. Study of the relationships between weather conditions and the marathon race, and of meteorotropic effects on distance runners. Int J Biometeorol. 1992;36(2):63-8. 32. Gaffen DJ, Ross RJ. Increased summertime heat stress in the US. Nature. 1998;396(6711):529. 33. Rosa RR. Extended workshifts and excessive fatigue. J Sleep Res. 1995;4:51-6. 34. Cohen S, Kamarck T, Mermelstein R. Perceived stress scale. Measuring stress: A guide for health and social scientists. 1994. 35. Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, et al. Development of a fatigue scale. J Psychosom Res. 1993;37(2):147-53. 36. Liversedge M. GoldenCheetah 2017 [cited 2018 June 4]. Available from: https://www.goldencheetah.org. 37. Tarvainen MP, Niskanen J-P, Lipponen JA, Ranta-aho PO, Karjalainen PA. Kubios HRV – Heart rate variability analysis software. Comput Methods Programs Biomed. 2014;113(1):210-20. 38. Tarvainen MP, Lipponen JA, Niskanen JP, Ranta-Aho PO. Kubios HRV (ver. 3.1) User's Guide: Kubios Oy Ltd.; 2018. 39. Gabb GM, Mangoni A, Anderson CS, Cowley D, Dowden JS, Golledge J, et al. Guideline for the diagnosis and management of hypertension in adults—2016. mortality. 2016;3:4. 40. Spierer DK, Rosen Z, Litman LL, Fujii K. Validation of photoplethysmography as a method to detect heart rate during rest and exercise. J Med Eng Technol. 2015;39(5):264-71. 41. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007;28(3):R1-39. 42. Hwang S, Seo J, Jebelli H, Lee S. Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker. Automation in Construction. 2016;71:372-81. 43. Fallow BA, Tarumi T, Tanaka H. Influence of skin type and wavelength on light wave reflectance. J Clin Monit Comput. 2013;27(3):313-7. 44. Malik M. Heart rate variability. Curr Opin Cardiol. 1998;13(1):36-44. 45. Zhong Y, Jan KM, Ju KH, Chon KH. Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability. Am J Physiol Heart Circ Physiol. 2006;291(3):H1475-83. 46. Johnson AT. Respirator masks protect health but impact performance: a review. J Biol Eng. 2016;10:4. 47. Hackett P, Roach R, Sutton J. High-altitude medicine. Wilderness medicine. 1995;5:1-36.

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Using Wearable Technology on Crime Scene Investigators to Monitor Position in a Crime Scene Elizabeth Morahan1, Giles Oakley2, Brendan Chapman1

1 Medical and Molecular Sciences, School of Veterinary and Life Sciences, Murdoch University. 90 South Street, Murdoch, WA, 6150. 2 School of Engineering and Information Technology, Murdoch University. 90 South Street, Murdoch, WA, 6150. ABSTRACT

Modern consumers have embraced wearable activity monitors, with sales expected to total 340 million units this year, worldwide. Devices can track a user’s location, as well as physiological measures such as heart rate. These functions have been used in various settings, including in elite sports, workplaces and clinical environments, and have the potential to be applied to crime scene investigation. Firstly, by utilising satellite positioning functions to monitor the geolocation of personnel, the need for crime scene entry/exit log may be made redundant as an individual is geotracked throughout the crime scene as the investigation progresses.

This study aimed to assess the ability of wearable activity monitors to track the geolocation of crime scene

investigators. To achieve this, we conducted a simulated, two-day clandestine grave body recovery

exercise with university Master students, while wearing an activity monitor, the Garmin fēnix® 5. Heatmap

analysis was conducted on the track points of participants. The accuracy of the GPS/GLONASS function was

tested using a Western Australian government verified datum point. The study found that the fēnix® 5 was

suitable for recording both participant’s gross movements within the crime scene, however more specific

results require further research.

Key Words: GPS, GLONASS, forensic, crime scene investigation, wearable technology, crime scene log.

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Table of Contents

1 List of Figures .......................................................................................................................................................... 72

2 List of Tables............................................................................................................................................................ 73

3 Introduction ............................................................................................................................................................ 74

4 Methods .................................................................................................................................................................. 75

4.1 Body Recovery Exercise .................................................................................................................................. 75

4.2 Generation of Heatmaps ................................................................................................................................. 75

4.3 Spatial Analysis ................................................................................................................................................ 76

4.4 Examination of Participant’s Tracks ................................................................................................................ 76

4.5 Accuracy .......................................................................................................................................................... 76

5 Results ..................................................................................................................................................................... 78

5.1 Heatmap Analysis ............................................................................................................................................ 78

5.2 Spatial Analysis ................................................................................................................................................ 81

5.3 Track analysis .................................................................................................................................................. 82

5.4 The accuracy of GPS/GLONASS Function ........................................................................................................ 84

6 Conclusion ............................................................................................................................................................... 85

7 References .............................................................................................................................................................. 86

8 Appendix ................................................................................................................................................................. 87

8.1 Participant 1 .................................................................................................................................................... 87

8.2 Participant 2 .................................................................................................................................................... 88

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1 LIST OF FIGURES

Figure 1. Haversine's Formula used to calculate distance between two coordinates12. ................................................ 77

Figure 2. Heatmaps for the movements of Participant 1 on Day One (top) and Day Two (bottom), with 2m, 5m and

10m buffer zones encircling the grave point1, 2. ............................................................................................................. 79

Figure 3. Heatmaps for the movements of Participant 2 on Day One (top) and Day Two (bottom) with 2m, 5m and

10m Buffer Zones encircling the grave point1, 2. ............................................................................................................. 80

Figure 4. Participant 2 Track for Day Two, unscaled (top) and scaled at 78 (bottom) ................................................... 83

Figure 6. Heatmap of Participant 1 on Day 1 (top) and Day 2(bottom) without buffer zone analysis1, 2. ...................... 87

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2 LIST OF TABLES

Table 1. Percentage of track points in relevant buffer zones over the two days for Participant 1. ............................... 81

Table 2. Percentage of track points in relevant buffer zones over the two days for Participant 2 ................................ 81

Table 3. The distance from the official datum of the two latitude and longitude obtained from the same point using

the Garmin fēnix® 5 ........................................................................................................................................................ 84

3

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3 INTRODUCTION

All crime scene investigation must remain free of contamination in order to ensure scene integrity is maintained. A

crime scene entry-exit log is used to monitor all personnel who enter and exit a cordoned crime scene3. Crime scene

entry-exit logs are standard practice for crime scene investigation worldwide. However, as these logs are compiled

by hand, there can sometimes be error associated with them4. Monitoring investigators automatically, and without

human intervention may help in reducing such error.

Wearable devices have grown in popularity in recent years, and are frequently fitted with GPS receivers5.

Smartphones and smart watches make use of this satellite technology, with smartwatches like the Garmin fēnix 5

employing both Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) to improve

tracking accuracy5. These devices have numerous applications, from monitoring the mobility elderly people6 to

tracking sports stars on the field7, and also could potentially be useful in eradicating the need for a crime scene log.

Impartial, continuous satellite tracking could improve the accuracy in the upkeep of crime scene logs, reducing

contamination risks, providing accountability for the wearer and eliminating a lengthy, tedious process of logging

movements.

There is very little research on potential applications for wearable technology in crime scene investigation, while the

use of such devices to track investigators is entirely novel. Therefore, this study aimed to establish if a GPS and

GLONASS enabled device, the Garmin fēnix® 58, can be used as a means of tracking an individual in a crime scene,

replacing the need for a crime scene entry and exit log.

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4 METHODS

4.1 BODY RECOVERY EXERCISE This study followed two Forensic Masters students in their early twenties, male and female, as they

conducted a clandestine body recovery exercise. Each participant was a member of a separate crime scene

team and were tasked with finding, excavating and processing a pig buried in soil at Whitby Falls Farm (at

1619 South Western Highway, Whitby, Western Australia), over two days, in a manner consistent with a

real-life investigation. Each group had a separate grave location.

Both participants wore a GPS and GLONASS enabled smartwatch, the Garmin fēnix® 5, for the entirety of

the two-day exercise. The ‘Track Me’ function was used, which recorded the wearer’s position for a

designated period of time, with both GPS and GLONASS receivers in use. The Garmin fēnix® 5 was used

separately to obtain a ‘geotag’, i.e. the specific coordinates of, of the grave site for each participant.

Additionally, the smartwatch was calibrated by geotagging a government datum of known coordinates,

Mundijong 169, which is present at the Whitby Falls Farm, twice.

4.2 GENERATION OF HEATMAPS The data was obtained on the watch through the Garmin BaseCampTM software10, version 4.6.2, and

exported into QGis1, version 3.03-Girona, an open source freeware, which was utilised for all heat map

analysis. The track and waypoints were converted into shapefiles, preserving all track data and a heat map

was constructed for each day. The heatmaps were then converted into TIF files and imported back into

QGis as a vector layer.

Satellite imagery from Google Earth®2 was used as a background to the heatmap, referencing the actual

geolocation of the study. The coordinates from each of the grave points were imported into QGis as a TIF

file.

The heatmaps were rendered with the following settings: a singleband pseudocolour colour ramp of black

to red, orange to yellow for all heatmaps, with interpolation set to linear and the mode set to quantile.

Black values represented areas of the lowest concentration of track points, while red, orange and yellow

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represented areas of increasingly high activity. For Participant 1, a scale of 1200 was consistently used

throughout the heat maps, while Participant 2 used 1300.

4.3 SPATIAL ANALYSIS QGis1 was also used to conduct spatial analysis. Zones of 2, 5 and 10 meters in distance from the grave

point were constructed from shapefiles using the buffer tool, and colour coded: green, grey and purple,

respectively for Participant 1, and dark grey, light grey with vertical lines and light grey with a black dashed

border. A count was taken of all the relative track points which fell into their respective buffer zones and

was recorded.

4.4 EXAMINATION OF PARTICIPANT’S TRACKS

The route taken through both days for both participants was opened in QGis1 with the aim of manually counting

routes in and out of the scene. A scale of 78 was used to enhance the visibility of the tracks. The crime scene logs

recorded contemporaneously by each participant were obtained with the intention of comparing entry-exit records

with the GPS/GLONASS tracks recorded by the Garmin fēnix® 5.

4.5 ACCURACY The coordinates of the two geotags were obtained from the Garmin BaseCamp10 software. The coordinates for the

datum, which were -32.291589, 116.012839, were obtained from the Australian government geodetic datum

website, Landgate11.

The distance between the two was calculated through an online Javascript which used the Haversine formula12. This

calculates the great-circle distance, or the shortest distance, between two points on a sphere13, and is given by the

following formula:

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Figure 7. Haversine's Formula used to calculate distance between two coordinates12.

where dlon is the difference in longitude and dlat is the difference in latitude.

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5 RESULTS

5.1 HEATMAP ANALYSIS Heatmap analysis combined with spatial analysis produced two heatmaps per participant: one for each day (Figures

2 and 3). The heatmaps without spatial analysis may be found in the Appendix for comparison purposes (Appendix

1).

Participant 1 has more black and dark red shading in day 1 in comparison to day 2, which indicates more areas of low

activity. This is consistent with the way in which a crime scene investigation is conducted. Initially, the crime scene is

searched thoroughly, before the focus narrows to an area of interest. Therefore, it is expected that there will be

areas that will be visited once during the search time period, and never visited again. The heatmaps of participant 1

appear to sprawl over the map on both days, in comparison to Participant 2, however (Figure 2). This may reflect the

use of different cut off values for the construction of the participant’s heatmaps, or increased GPS error in

Participant 1.

The change in focus to the gravesite is evident in Participant 2 (Figure 3). The heatmap spread narrows considerably

in day two, and the areas of yellow high activity as the crime scene team focused on the clandestine grave site. The

hotspot appears just to the left of the grave point in day one, however by day two, the hotspot has aligned almost

directly with the grave point.

This is less clear in Participant 1, who has multiple hotspots. These appear to correlate with several main activity

areas, including: a basecamp where notetaking was conducted, the area in which the participant’s car was located

and a separate space where lunch was taken.

Isolated track points are visible throughout all heatmaps, most notably on day one of participant 1 (figure 1). This is

most likely due to satellite issues. A phenomenon similar to that of urban canyons, where a GPS receivers incorrectly

position, due to the reflection of signal from tall buildings, may be the cause of this14 . This calls into question the

accuracy of satellite tracking, as precision and accuracy is required for crime scene investigation logs.

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Figure 8. Heatmaps for the movements of Participant 1 on Day One (top) and Day Two (bottom), with 2m, 5m and 10m buffer zones encircling the grave point1, 2.

Heatmap Day Two

Low Activity

High Activity

Low Activity High Activity

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Low Activity

High Activity

Low Activity

High Activity

Day Two Track Points

Figure 9. Heatmaps for the movements of Participant 2 on Day One (top) and Day Two (bottom) with 2m, 5m and 10m Buffer Zones encircling the grave point1, 2.

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One study, however, has shown that the accuracy of GPS devices operating in urban canyons may be improved by

implementing an algorithm while mapping the data14. This could be adapted for use in tree dense areas, and future

study should be focused in this direction.

The information contained in these heatmaps has practical usage in crime scene investigation. As suggested in

research by Baber et al., GPS data could be used to facilitate training of new investigators15-18. The heatmaps

obtained would be excellent for this purpose, as they could be analysed further to obtain patterns in the

investigation of specific crimes. Potentially, they could also be used to monitor employee time management due to

the visual portrayal of time spent at various locations.

5.2 SPATIAL ANALYSIS The spatial analysis generated the number of track points in each buffer zone. These are represented as percentages

in Tables 1 and 2.

Table 5. Percentage of track points in relevant buffer zones over the two days for Participant 1.

Table 6. Percentage of track points in relevant buffer zones over the two days for Participant 2

Both participants showed a marked increase in time spent near the grave point in both participants from day one

and two (Tables 1 and 2). This reflects the narrowing of focus in scene examination, from a broad, preliminary

search, to pinpoint the hotspot of criminal activity, in this case, the location of the clandestine grave.

The broad applicability of this analysis means it could be applied to a method of automating the entry-exit process.

Buffer zones can be of varying widths, as demonstrated above, so can capture points within a narrow or wide-

reaching range as needed. New research could focus on creating a process that uses these zones to log the

BUFFER ZONE DAY ONE DAY TWO

2 METRES 0.74% 1.02% 5 METRES 4.09% 10.26% 10 METRES 17.22% 48.49%

BUFFER ZONE DAY ONE DAY TWO

2 METRES 0.73% 5.35% 5 METRES 2.80% 23.65% 10 METRES 12.72% 64.15%

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movements of an investigator. It would be beneficial to determine if points could be divided according to whether

they were left while entering the zone or while exiting. This would fulfil the criteria needed for the maintenance of a

fully-automated crime scene entry-exit log.

5.3 TRACK ANALYSIS Track analysis produced maps that featured the participant’s movements against corresponding satellite imagery. An

example from Participant 1, on day two, is given for demonstration purposes (Figure 4).

Unfortunately, differentiating the entry and exit movements could not be achieved due to the sheer volume of

tracks, as well as the location of the entry-exit points. This also raises a point for consideration: tracking investigators

in a real-life crime scene would result in a considerable volume of data that would need to be managed efficiently

and accurately. A more sophisticated method is required if GPS/GLONASS data is to replace manual log, therefore.

The construction of buffer zones around the designated entry and exit point, as discussed above, is a potential

option.

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Figure 10. Participant 2 Track for Day Two, unscaled (top) and scaled at 78 (bottom)

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5.4 THE ACCURACY OF GPS/GLONASS FUNCTION The distance between the coordinates of the two geotags of the same point are given in the following table (Table

3):

Table 7. The distance from the official datum of the two latitudes and longitude obtained from the same point using the Garmin fēnix® 5

LATITUDE LONGITUDE DISTANCE FROM DATUM

GEOTAG 1 -32.291583 116.012883 4m GEOTAG 2 -32.291483 116.012633 23m

In Geotag 1, there was a 4m difference between the coordinates given by the smartwatch and those of the officially

recorded datum. This is within the range of acceptable error for a GPS receiver19. Geotag 2. However, it was found

that the distance between the Garmin fēnix® 5’s recorded coordinates and the datum’s, was 23m. This is well

outside the recommended error of 7.5m or below19.

There could be many explanations for the difference in accuracy. A probable explanation is that tree cover blocked

the signal from the device to the GPS and GLONASS receivers, as described above. Additionally, the sample size is

very small, and either obtained error could be an outlier.

The formula itself assumes that the Earth itself is a perfect sphere13. In reality, however, the Earth is ellipsoidal, and

this assumption means that trans-polar distances are overestimated, while trans-equatorial distances are

underestimated12. In geodetics, Vincerty’s formula is generally used, which operates under the assumption that

Earth is ellipsoidal, and can have an accuracy of within 0.5mm20. This method would return a more accurate

estimation of any error present, but due to time constraints, was unable to be used.

A future study could also employ a different method for obtaining the accuracy of the GPS enabled device. As the

function of the device involves movement, it would be more useful to obtain data during movement. A datum could

be used to form the corner of a measured square, which a participant then walks along with the device enabled. The

distance from the obtained track points and the line of the measured square could then be obtained. This would give

an assessment of error that would more closely resemble real life usage, and form a larger sample size.

If GPS/GLONASS enabled devices are to be used in crime scene investigation, accuracy would need to be near

perfect. Therefore, further study needs to be conducted on the accuracy of these devices if they are to be used in

crime scene investigation.

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6 CONCLUSION

Crime scene logs are a crucial part of maintaining the integrity of a crime scene and minimising the risk of

contamination. If there are errors within these logs, the entire investigation could be compromised. Human error

and bias could be avoided by fully automating the process by tracking crime scene investigators using wearable

devices.

This study is a preliminary step towards incorporating Global Navigational Satellite Systems such as GPS and

GLONASS into crime scene investigation. It was found that it was possible to monitor the movements of crime scene

investigators with GPS/GLONASS enabled wearable devices. In addition, information could be obtained on where a

participant spent the majority of the time in a scene, as well as the number of times a person enters a defined area.

These types of data all have potential applications in the scope of crime scene investigation.

While the data obtained could prove useful for management of employees as well as potentially eliminating the

need for a manual crime scene log in the future, further research should be conducted on the accuracy of these

devices, as this study was limited by the small sample size and time constraints.

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7 REFERENCES

1. Sherman GE. QGIS 3.0.3-Girona. 3.03-Girona ed: Open Source; 2018. 2. Google. Google Earth Pro 7.3.1.4507. 2018. 3. ACPO. Murder Investigation Manual [Internet]. Wyboston: National Centre for Policing Excellence; 2006 [cited 2018 February 20]. Available from: https://www.app.college.police.uk/app-content/investigations/linked-reference-material/. 4. Ryan TG, Overlin TK, editors. Human reliability assessment: tools for law enforcement. Training, Education, and Liability Issues for Law Enforcement Scientists and Engineers; 1997: International Society for Optics and Photonics. 5. Hoelzl G, Kranz M, Schmid A, Halbmayer P, Ferscha A, editors. Size does matter - positioning on the wrist a comparative study: SmartWatch vs. SmartPhone. 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops); 2017 13-17 March 2017. 6. Shoval N, Auslander G, Cohen-Shalom K, Isaacson M, Landau R, Heinik J. What can we learn about the mobility of the elderly in the GPS era? Journal of Transport Geography. 2010;18(5):603-12. 7. Wisbey B, Montgomery PG, Pyne DB, Rattray B. Quantifying movement demands of AFL football using GPS tracking. J Sci Med Sport.13(5):531-6. 8. Garmin. Garmin fēnix® 5: Garmin; 2018 [cited 2018 April 10]. 9. Western Australian Land Information Authority. Web Mapping Service: Western Australian Government; 2016 [updated 2016; cited 2018 June 1]. Available from: https://catalogue.data.wa.gov.au/dataset/geodetic-survey-marks-point/resource/e568c43f-fa58-3479-9c3d-f7ecf1fce403. 10. Garmin. Garmin BaseCamp v 4.6.2. 2018. 11. Landgate. Geodetic Survey Marks Web Mapping Service [Internet]: Western Australian Government; 2016 [cited 2018 March 13]. Available from: https://catalogue.data.wa.gov.au/dataset/geodetic-survey-marks-point/resource/e568c43f-fa58-3479-9c3d-f7ecf1fce403. 12. Hedges A. Finding distances based on Latitude and Longitude 2018 [cited 2018 June 8]. Available from: https://andrew.hedges.name/experiments/haversine/. 13. Robusto CC. The cosine-haversine formula. The American Mathematical Monthly. 1957;64(1):38-40. 14. Ben-Moshe B, Elkin E, Levi H, Weissman A, editors. Improving Accuracy of GNSS Devices in Urban Canyons. CCCG; 2011. 15. Baber C, editor Wearable technology for crime scene examination: distributed cognition and naturalistic decision making. Proceedings of the 9th International Conference on Naturalistic Decision Making; 2009. 16. Baber C, Smith P, Panesar S, Yang F, Cross J, editors. Supporting Crime Scene Investigation2007; London: Springer London. 17. Baber C, Smith P, Butler M, Cross J, Hunter J. Mobile technology for crime scene examination. Int J Hum Comput Stud. 2009;67(5):464-74. 18. Baber C, Smith P, Cross J, Hunter JE, McMaster R. Crime scene investigation as distributed cognition. Pragmatics & Cognition. 2006;14(2):357-85. 19. Kaplan E, Hegarty C. Understanding GPS: principles and applications: Artech house; 2005. 20. Vincenty T. Direct and Inverse Solutions of Geodesics on the Ellipsoid with Application of Nested Equations. Survey Review. 1975;23(176):88-93.

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Figure 11. Heatmap of Participant 1 on Day 1 (top) and Day 2(bottom) without buffer zone analysis1, 2.

8 APPENDIX

8.1 PARTICIPANT 1

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Figure 7. Heatmap of Participant 1 on Day 1 (top) and Day 2(bottom) without buffer zone analysis1, 2.

PARTICIPANT 2

Low Activity

High Activity

Documents

WEARABLE ACTIVITY MONITORS ON CRIME SCENE … · positioning. Smartwatches are one such modern wearable device and are expected to make up 60% of the forecast $25 billion wearables