View
221
Download
0
Category
Tags:
Preview:
Citation preview
Addressing Stress and Addictive Behavior in the Natural Environment
Using AutoSense
Santosh Kumar Computer Science, University of Memphis
Our Team
Behavioral Science Engineering
04/21/23Santosh Kumar, University of Memphis2
Dr. Mustafa al’Absi, UMN Dr. J Gayle Beck, Memphis Dr. David Epstein, NIDA, NIH Dr. Tom Kamarck, Pittsburgh Dr. Satish Kedia, Memphis Dr. Kenzie Preston, NIDA, NIH Dr. Marcia Scott, NIAAA, NIH Dr. Saul Shiffman, Pittsburgh Dr. Annie Umbricht, Johns
Hopkins Dr. Kenneth Ward, Memphis Dr. Larry Wittmers, UMN
Dr. Anind Dey, CMU Dr. Emre Ertin, Ohio State Dr. Deepak Ganesan, UMass Dr. Greg Pottie, UCLA Dr. Justin Romberg, Georgia
Tech Dr. Dan Siewiorek, CMU Dr. Asim Smailagic, CMU Dr. Mani Srivastava, UCLA Dr. Linda Tempelman, Giner
Inc. Dr. Jun Xu, Georgia Tech
Students & Postdocs
Memphis CMU, OSU, UCLA, Georgia Tech., UMN
04/21/23Santosh Kumar, University of Memphis3
Dr. Andrew Raij (now at USF)
Dr. Kurt Plarre Dr. Karen Hovsepian Amin Ahsan Ali Santanu Guha Monowar Hussain Somnath Mitra Mahbub Rahman Sudip Vhaduri
Dr. Motohiro Nakajima, UMN
Patrick Blitz, CMU Brian French, CMU Scott Frisk, CMU Nan Hua, Georgia Tech Taewoo Kwon, OSU Moaj Mustang, UMass Siddharth Shah, OSU Nathan Stohs, OSU
Paradigm Shift in Disease Prevalence
04/21/23Santosh Kumar, University of Memphis4
Infectious diseases, and those from poor hygiene & nutrition not as prevalent
They are replaced by diseases of slow accumulation Heart diseases Cancer, Ulcer Depression, Migraine
Growing Epidemic – Stress & Addiction
04/21/23Santosh Kumar, University of Memphis5
Stress & addictive behavior lead to or worsen diseases of slow accumulation Stress: headaches, fatigue, heart failures,
hypertension, depression, addiction, anxiety, rage
Smoking: cancer, lung diseases, heart diseases Yet, both continue to be widespread
Stress: 43% adults suffer adverse health effects Smoking: responsible for 20% of deaths in US
An urgency to help individuals reduce stress & abstain from addictive behavior
Addressing Stress & Addiction
04/21/23Santosh Kumar, University of Memphis6
An unobtrusively wearable sensor suite called AutoSense So, individuals can wear it in natural environment
Robust inference of stress from physiological measures Automatically measure physiological and psychological stress
Automatic inference of addictive behaviors Smoking, drinking, drug usage from sensor measurements
Detect addiction urges to provide timely intervention Craving for smoking and drug usage Contexts/cues that may lead to craving and eventual relapse
Infer other moderating behavioral & social contexts Conversation, physical activity, traffic stressors, etc.
Outline
04/21/23Santosh Kumar, University of Memphis7
Hardware and Software Platforms AutoSense sensor suite FieldStream mobile phone framework
Inferring Stress Detecting stress from physiology Predicting perceived stress
Ongoing User Studies Detecting smoking, drinking, craving, drug usage,
etc. Roadmap & Long-term Vision
AutoSense Wearable Sensor Suite
04/21/23Santosh Kumar, University of Memphis8
Chestband sensors: ECG, Respiration, GSR, Ambient & Skin Temp. , Accelerometer
Armband sensors: Alcohol (WrisTAS) , GSR, Temp., Accelerometer
Android G1 Smart Phone
Key Features of AutoSense Hardware
04/21/23Santosh Kumar, University of Memphis9
Ultra low power Six sensors (ECG, GSR, Resp., Temp, Accel) consume 1.75 mA Overall current consumption < 3mA (for 10+ days of lifetime)
Sampling and transmission of 132 samples/sec (i.e., 1.8 kbps)
Reliable radio ANT with integrated quality of service and duty cycling
Reliable and timely wireless transmissions in crowding scenarios Antenna impedance is matched for human body
Power loss reduced from 33% (for free space configuration) to 0.1% Operates at 2480-2524 MHZ band to be immune to Wi-Fi
Average packet loss rate of 0.57% even when Wi-Fi activity is intense
FieldStream – Mobile Phone Framework
04/21/23Santosh Kumar, University of Memphis10
For use in conducting scientific user studies In both supervised lab settings and in uncontrolled field
settings It collects measurements
Sensor measurements from wearable and phone sensors Self-reports from subjects
Computes tens of features and various statistics over them (e.g., HR, HRV, RR, Minute Ventilation)
Makes inferences using machine learning algorithms Stress, posture, activity, conversation, and commuting Detects sensor detachments and loosening
Is reconfigurable So, no need for change in source code for use in a new user
study
04/21/23Santosh Kumar, University of Memphis11
Converts stream of sensor measurements into packets & delivers to intended recipient
Provides a common interface to all sensors & populates buffers for feature computation
Computes base features (e.g., R-R interval) & statistics over them
Deployment Experiences and Findings
04/21/23Santosh Kumar, University of Memphis12
21 subjects in UMN - completed Lab session on stress; 10-14 hours per day for 2 days in
field 36 subjects in Memphis - completed
3 consecutive days in field with daily visits to the lab
Some findings on human behaviors in our subject pool Stress occurrence in daily life (Plarre et. al., in ACM IPSN’11)
Subjects were psychologically stressed 26-28% of time Natural conversations (Rahman et. al., in ACM Wireless
Health’11) Frequency of conversations : 3 per hour Avg. duration of a conversation: 3.82 minutes Avg. Time between conversations: 13.3 minutes
Outline
04/21/23Santosh Kumar, University of Memphis13
Hardware and Software Platforms AutoSense sensor suite FieldStream mobile phone framework
Inferring Stress Detecting stress from physiology Predicting perceived stress
Ongoing User Studies Detecting smoking, drinking, craving, drug usage,
etc. Roadmap & Long-term Vision
Measuring Stress in the Field Self-reports have been used for a long time
Questionnaires or surveys Measures perceived stress
Strengths and limitations (+) Captures detailed information (+) Proximal predictor of mental health (-) Distal predictor of physical health (-) Discrete sampling (-) Burden to participant
Need an automated approach for continuous stress measurement in the field
14 Santosh Kumar, University of Memphis 04/21/23
Continuous Measure of Stress
04/21/23Santosh Kumar, University of Memphis15
Can use physiological measurements to assess stress, but
Physiology is affected by several factors, not only stress
How to map physiology to psychology?
Activity, change in posture, speaking, food, caffeine, drink, etc.
How to separate out the changes in physiology due to stress?
The Quest for Automated Stress Measure
Predicting psychological state from physiology William James – pioneering work (1880) John Cacioppo and others – revitalized interest (1990)
Several studies on emotion and stress prediction Identified physiological markers of stress and emotion
Example: Heart rate, skin conductance response But, confined to controlled settings
Few studies in uncontrolled environments M. Myrtek’96 , J. Healey’05, J. Healey’10, Either no validated stressors, no lab session to train
models, not able to account for confounders, or tried to match self-reports directly
16 Santosh Kumar, University of Memphis 04/21/23
In the AutoSense Project We developed a new wearable sensor suite Conducted a scientific study with validated stress
protocol 21 participants, 2 hour lab study, 2 day field
study Protocol designed by behavioral scientists Stressors used are validated and known to produce stress Self-reports designed by expert behavioral scientists
Developed new stress models to measure Physiological response to stress
To measure adverse physiological effects of stress Perception of stress in mind
To derive a continuous rating of perceived stress
17 Santosh Kumar, University of Memphis 04/21/23
Lab Study – Stress Protocol 2 hour lab session
Subjects exposed to three types of stressors Public speaking – psychosocial stress Mental arithmetic – mental load Cold pressor – physical stress
Physiological signals recorded at all times Using AutoSense
Also, collected self-reported stress rating 14 times
18
Baseline
10 Min 10 10 4 4 4 4 4 4 4 10 10 104 555
RecoveryPublic
Speaking Cold
Pressor
Start End
Mental Arithmetic
Santosh Kumar, University of Memphis 04/21/23
Self-Report Measures of Stress
Self-report questions related to affective state
Santosh Kumar, University of Memphis19
Question Possible Answer Code
Cheerful? YES yes no NO 3 2 1 0
Happy? YES yes no NO 3 2 1 0
Frustrated/Angry? YES yes no NO 0 1 2 3
Nervous/Stressed? YES yes no NO 0 1 2 3
Sad? YES yes no NO 0 1 2 3
04/21/23
04/21/23Santosh Kumar, University of Memphis20
Our Aproach
04/21/23Santosh Kumar, University of Memphis21
Identified 22 Features from Respiration
22
Inhalation Duration
Exhalation Duration
Respiration Duration
Stretch
Mean
Median
Quartile Deviation
80th Percentile
Breathing Rate
Minute Ventilation
Insp./Exp. Ratio
Basic Features Statistical Features
Santosh Kumar, University of Memphis 04/21/23
Computed 13 Features from ECG
23
Mean
Median
Quartile Deviation
80th Percentile
RR Intervals Power in low/medium/high frequency bands
Ratio of low frequency/high power
Variance
RSA
Basic Features Statistical Features
Santosh Kumar, University of Memphis 04/21/23
Feature and Classifier Selection
Used Weka for Training Evaluated Decision Tree, DT with Adaboost, and
Support Vector Machine Using 10-fold cross validation, and training/test data
Classification results using 35 features
After feature selection, 13 features 8 Respiration, 5 ECG
24
J48 Decision Tree
J48 with Adaboost
SVM
87.67% 90.17% 89.17%
Santosh Kumar, University of Memphis 04/21/23
Classification Accuracy on Lab Data
25 Santosh Kumar, University of Memphis 04/21/23
Our Aproach
04/21/23Santosh Kumar, University of Memphis26
Perceived Stress Model Use a binary Hidden Markov Model
To reduce number of parameters, we approximate by
models the gradual decay of stress with time models the accumulation of stress in mind due to
repeated exposures to stress Both and are person dependent and are
learned from self-reported ratings of stress27
stress perceived is 1,0ts
,ˆˆ 1 ttt x
ts
tx valuestress perceived is ,,|1P 11 ttt xxs
t
Santosh Kumar, University of Memphis 04/21/23
Evaluation of the Model (on Lab Data)
Correlation of perceived stress model and self-report rating in the lab session
Over 21 participants
Median correlation 0.72
28 Santosh Kumar, University of Memphis 04/21/23
Field Study Protocol Participants wore AutoSense continuously for 2
days Going about their daily life (home, school, etc.) Except when sleeping at night
Field self-reports Participants responded to self-reports 20+ times each
day Same questions about affect state as in the lab
Additional context information
Additional behaviors automatically collected Speaking, from respiration patterns Physical activity, from accelerometer
29 Santosh Kumar, University of Memphis 04/21/23
Realities of Natural Environment
30
Data eliminated 37% affected by
activity 30% by bad
quality
Less than 4 min consecutive data
4 subjects missing data or self-report
Santosh Kumar, University of Memphis 04/21/23
Evaluation of the Model (Field)
31
Compared average stress ratings over both days
Accumulation model versus self-report
Linear interpolation
Santosh Kumar, University of Memphis 04/21/23
Outline
04/21/23Santosh Kumar, University of Memphis32
Hardware and Software Platforms AutoSense sensor suite FieldStream mobile phone framework
Inferring Stress Detecting stress from physiology Predicting perceived stress
Ongoing User Studies Detecting smoking, drinking, craving, drug usage,
etc. Roadmap & Long-term Vision
Ongoing User Studies
04/21/23Santosh Kumar, University of Memphis33
Memphis Study 40 daily smokers and social drinkers A lab study followed by one week in the field
Stress, drinking, smoking, and craving for cigarettes marked National Institute on Drug Abuse (NIDA) Study
20 drug addicts undergoing treatment Two lab sessions and 4 weeks in field
Smoking, craving, and stress marked in lab; Craving, stress, and drug usage reported in the field
Johns Hopkins Study 10 drug addicts in residential treatment Drug injection in lab, daily behaviors marked in the
field To develop detectors for smoking, craving, and
drug usage
Roadmap
04/21/23Santosh Kumar, University of Memphis34
The near-term goal is to develop personalized stress and addiction assistants on the mobile phone to Help reduce stress, e.g., least stressful route for driving Break addiction urges where and when they occur
But, these applications will impact someone’s health Will it indeed be helpful to each user and not hurt anyone?
Will it help maintain healthy behaviors even after the novelty phase? How do we generate evidence for its validity, efficacy, safety?
Within reasonable time and effort, unlike multiyear RCTs How do we design it so it has greater chance of success?
Various theories exist (e.g., stages of change, social cognitive theory)
But, no overall theory for designing adaptive interventions exist today
Long-term Vision
04/21/23Santosh Kumar, University of Memphis35
Use these experiences to discover the scientific principles that can be used broadly in mobile health (mHealth) To design and develop
New mHealth measures that are robust enough for field usage
New mHealth treatments and interventions that work To generate evidence of validity, efficacy, and
safety of mHealth
Contribute to the newly emerging science of mHealth
Further Reading
04/21/23Santosh Kumar, University of Memphis36
1. E. Ertin, N. Stohs, S. Kumar, A. Raij, M. al'Absi, T.Kwon, S. Mitra, Siddharth Shah, and J. W. Jeong, “AutoSense: Unobtrusively Wearable Sensor Suite for Inferencing of Onset, Causality, and Consequences of Stress in the Field,” ACM SenSys, 2011.
2. Md. Mahbubur Rahman, Amin Ahsan Ali, Kurt Plarre, Mustafa al'Absi, Emre Ertin, and Santosh Kumar, “mConverse: Inferring Conversation Episodes from Respiratory Measurements Collected in the Field,” ACM Wireless Health, 2011.
3. Mohamed Mustang, Andrew Raij, Deepak Ganesan, Santosh Kumar and Saul Shiffman, “Exploring Micro-Incentive Strategies for Participant Compensation in High Burden Studies,” to appear in ACM UbiComp, 2011.
4. K. Plarre, A. Raij, M. Hossain, A. Ali, M. Nakajima, M. al'Absi, E. Ertin, T. Kamarck, S. Kumar, M. Scott, D. Siewiorek, A. Smailagic, and L. Wittmers, “Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment,” ACM IPSN, 2011.
5. Andrew Raij, Animikh Ghosh, Santosh Kumar and Mani Srivastava, “Privacy Risks Emerging from the Adoption of Inoccuous Wearable Sensors in the Mobile Environment,” In ACM CHI, 2011.
Nominated for best paper award
Nominated for best paper award
Outline
04/21/23Santosh Kumar, University of Memphis37
Hardware and Software Platforms AutoSense sensor suite mStress mobile phone framework
Inferring Stress Detecting stress from physiology Predicting perceived stress
Ongoing User Studies Detecting smoking, drinking, craving, drug usage,
etc. Privacy Issues in mHealth research
Behavior Revelation from Sensors
04/21/23Santosh Kumar, University of Memphis38
Accelerometer & gyroscopes can be used to monitor activity level Can infer movement pattern and place from these sensors
See SenSys’10 paper on AutoWitness Could also infer epileptic seizures
Respiration sensor can be used for activity monitoring or estimating the extent of pollution exposure Can use it to infer conversation, smoking, and stress Inferring of public speaking episodes could even pinpoint
the identity of the subject Development of other behavioral inferences in
progress
How Concerned are Study Participants?
04/21/23Santosh Kumar, University of Memphis39
Conducted a 66 subject (36 in NS) study
Evaluated their concern level as their personal stake in the data is increased
Also, how their concern level changes as modalities are added/removed
Awareness & Concern
04/21/23Santosh Kumar, University of Memphis40
Sharing of stress, commuting, and conversation generate higher concern than the sharing of place
Effect of Privacy Transformations
04/21/23Santosh Kumar, University of Memphis41
Disassociating time is more critical than disassociating place of occurrance
Even reducing timestamp to duration helps
Recommended