M. Wagner1, Y · Web viewThis study was motivated by two elements, first is real life pilot report, the second is more scientific. Important but non-scientific source for the “Grip

GRIP FORCE AS A PHYSIOLOGICAL MEASURE OF STRESS IN TRACKING TASKS

M. Wagner1, Y. Sahar2, T. Elbaum2 and E. Berliner3

1 Department of Industrial Engineering & management, Ariel University, Ariel, 44837, Israel.

2 Faculty of Industrial Engineering & management, Technion, Haifa, 32000, Israel.

3 Department of Management, Bar Ilan University, Ramat-Gan, 5290002, Israel.

Abstract

Objective stress measures in psychomotor tasks, such as flight simulator training, are strongly needed. The Yerkes-Dodson curve (inverted U relationship curve between performance and stress) requires monitoring of stress level if training is to be optimal. Here we examined the feasibility of joystick Grip Force as a stress measure. Nine male participants performed 2D tracking tasks composed of 3 dynamic target maneuvers, with 4 constant-velocity levels, and a secondary monitoring task. Course credits conditioned by performance level served as additional stressor. Galvanic Skin Response served as physiological validation measure. The Grip-Force sensor was embedded in the joystick grip, concealed from participants’ awareness. Our results support the feasibility of Grip-Force as a valid and reliable stress measure. Contrary to other physiological stress sensors, grip-embedded Grip Force sensor is “transparent” to the measured person and technically simple. Various applications and limitations of the Grip Force measurement tool are discussed.

Keywords: Stress, Grip-Force, GSR, Psychomotor, Tracking.

1 Introduction

1.1 Motivation

This study was motivated by two elements, first is real life pilot report, the second is more

scientific. Important but non-scientific source for the “Grip Force” measure, originates

from unpublished military aviation medicine studies, and reported pilot experience. Pilots

are fully aware of their stick-grip-force responses in stressful flight scenes. Among pilots,

the “white finger syndrome” is well known, describing the extreme stress response, where

the pilot’s fingers turn white resulting from extreme muscle tension. The second

motivating element is the need for an objective and suitable stress measure to be applied in

training facilities such as flight simulators. The idea of “Grip Force” as candidate measure

stems from scientific research findings showing increased muscle-tonus as one of

Wagner, Sahar, Elbaum & Berliner

spontaneous stress responses (Balson, Howard, Manning & Mathison, 1986; Bozdemir,

Sarica & Demirkiran, 2002; Liao, Zhang, Zhu, Ji & Gray, 2006).

Training can be a very expensive and hazardous task, especially in the aviation field.

Flight simulators are designed to solve these problems and to supplement the training

procedure, providing a safe training environment in which extreme situations can be

trained (Moroney & Moroney, 1999). However, simulator training time may still be an

expensive resource (Smith, Gevins, Brown, Karnik & Du, 2001) and therefore training

efficiency is highly valued (Salas, Bowers & Rhodenizer, 1998).

At the beginning of the 20th century Yerkes and Dodson argued that there is an inverted

U shaped relationship between stress and motor task performance. Low or high sress

values impair performance while mid range stress values are optimal for motor skill

performance (Kramer & Weber, 2000). This phenomenon, which is called "Inverted U",

has been demonstrated in many cases (Anderson, 1976; Hancock & Warn, 1989).

Therefore, the need for a simulator- integrated stress measurement tool, providing real time

stress values during Flight Simulator (FS) runs is self-evident. Such a tool could aid

identification of the optimal stress point, and in turn maximize training-process efficiency.

Some physiological stress measurement methods are well astablished, such as: Galvanic

Skin Response (GSR), Heart Rate (HR), Pupil Diameter (PD) and more (Gopher &

Donchin, 1986; Yerkes & Dodson, 1908). Although widespread of these methods, they are

unsuitable for the flight simulator environment, due to the following main reasons:

First, these measures are complex and require a subtle integration process into the FS

environment (Fournier, Wilson & Swain,1999; Smith, Gevins, Brown, Karnik & Du, 2001;

Van Orden, Limbert, Makeig & Jung, 2001). Second, the output of these tools requires an

"over time" statistical analysis getting at the stress value, therefore not allowing a quick

and real time value of the trainee's stress level (Benoit et al., 2009). Third, these measures

are characterized by having some delay in reading the stress signals and therefore making

online measurement impossible (Benoit et al., 2009). Forth, most of the physiological

measures are salient to the trainee and therefore may cause interference to the main

training simulated task performance (Van Nimwegen & Uyttendaele, 2009). The Force


Sensitive Resistance (FSR) is a measure of Grip Force. This measure overcomes most of

the shortcomes of the conventional physiological stress measures.

The FSR senses the force exerted upon the joystick or steering wheel surface by the

trainee’s gripping hand. Since stress manifests an increased muscle tonus (Balson, Howard,

Manning & Mathison, 1986; Bozdemir, Sarica & Demirkiran, 2002; Liao, Zhang, Zhu, Ji

& Gray, 2006), measuring the grip force may indicate the level of the trainees’ stress.

The FSR measure is characterized by its immediate response. In addition, its

manifestation is dynamic and variable. Here, the force sensor was integrated in the flexible

fiber cover of the control grip, while participants were totaly unaware of its existance. This

simple tool could be easily integrated in flight simulators as well as other types of trainers.

The Grip Force measure -output can be easily interpreted into real time levels of trainee’s

stress. Finally, this measure's face validity is strong, comparing to the other stress measures

mentioned earlier.

1.2 Stress

Hans Selye (1936) initially defined stress. According to this definition, a percieved threat

to an organism raises the need to mobilize resources in order to enable it to react, in one of

three possible ways of action: fight the threat, flee away from it or freeze (hoping to go

unnoticed).

The two most dominant theories concerning stress stem from cognitive or physiological

explanation of behavior. According to the cognitive approach, stress is the outcome of a

gap between a subject's perception of a threatening situation's demands and his perception

of the available resources to cope with it (Lazarus, 1966; Lazarus & Launier, 1978). Later

definitions emphasize the role of the perceived importance of success (Staal, 2004). These

theories underlie the cognitive interpretation of the Stressful stimulus.

The physiologic approach to stress defines it as a threatening situation that stimulates

the hypothalamic-pituitary-adrenal axis (the HPA axis, a major part of the neuroendocrine

system that controls reactions to stress) and the sympathetic system, resulting with the

release of stress hormones, which influence the organism's behavior in order to help it cope

with the threat (De Kloet, Joëls & Holsboer, 2005). These theories underlie the

physiological reaction to the Stressful stimulus.


It should be emphasized that both approaches refer to performance when discussing

stress.

1.3 Performance

Performance is defined as the accomplishment of a given task measured against pre-set

known standards of accuracy, completeness, cost, and velocity. Performance measures are

typically associated with one of four categories: measures of velocity or time, measures of

accuracy or error, measures of workload or capacity demands and measures of preference

(Wickens & Hollands, 2000).

1.4 Stress and performance

According to the Yerkes–Dodson law, performance increases with physiological or mental

stress up to a certain point. When levels of stress become too high, performance decreases

(Kramer & Weber, 2000). This approach argues that optimal performance is acheived at

the central levels of stress. Thus, In order to ensure that the stress levels are optimal during

task execution one has to monitor the level of stress of the subject.

1.5 Stress measurement

One of the most common methods of measuring stress is through self report measures,

namely questionnaires. However, this method has been criticized for discrepancies

between its results and the result of validated physiological measures (Bourne & Yaroush,

2003; Gopher & Braune, 1984).

Another method of measuring stress is based on the physiological definition of stress.

Since the endocrine system has a major role in the physiological stress mechanism,

measurement of stress related hormones may be the most accurate and unequivocal

methods of measuring stress (Baum & Grunberg, 1995). However, this procedure is

expensive and may take a very long time and cost a large sum.

One of the most prevalent measures of stress is the physiological GSR measure

(Kramer, 1991). It is relatively easy to use, but its sensitivity to varied psychological

arousals makes it difficult to distinguish between stress and emotions such as sorrow. A

possible solution for this challenge is making sure that the task at hand is designed to


induce stress, by creating a psychological threat or danger (Lazarus & Eriksen, 1952).

Therefore, a threatening and dangerous situation that is followed by a rise in the GSR

measure may lead to the conclusion that there is a rise in the level of stress (Perala &

Sterling, 2007).

1.6 Hypotheses

Hypothesis 1: In line with the physiological approach, and since stress is manifested by

increased muscle tonus, we hypothesize a positive linear correlation between FSR and

GSR, so that the higher the value of the FSR measurement, the higher the value of the

GSR.

Hypothesis 2: In order to validate task difficulty manipulation, we hypothesize that

complex target manoeuvres and higher target velocities produce lower performance (are

more difficult task conditions).

Hypothesis 3: In line with the cognitive approach, and given that hypothesize 2 is

confirmed, we hypothesize that difficult task conditions (complex target manoeuvres and

higher target velocities) induce higher FSR measurement values.

2 Method

2.1 General

In a specially arranged laboratory setup, participants used a joystick for tracking a 2D-

manoeuvring target on a screen, and simultaneously performed a monitoring task using the

joystick trigger, as a secondary task. Each trial lasted 45sec. Levels of tracking difficulty

were manipulated by 4 levels of target velocity, and 3 modes of target manoeuvring

profiles.

A GSR electrode was attached to the participants’ left-hand pointing finger. A special

sensor recorded joystick-grip-force without Participants’ awareness. All tracking data was

recorded for later statistical analysis. Participants were rewarded for their participation by

bonus points in an academic course grade, conditioned to their individual performance.


This added “psychological threat” in line with the cognitive approach to stress, and served

as an additional stressor.

2.2 Participants

The participants were 9 male industrial engineering bachelor students, aged 20-30 years,

all right handed and normal or corrected eye sight. The participants were not “gamers”

(didn’t spend more than four weekly hours engaging in similar tasks such as computer

games and domestic flight simulators). Participants who did not reach performance

criterion (see section 2.5 - procedure) were ruled out so that the participants reach a

uniform level of medium to high performance at the beginning of the experiment. Thus,

preventing the effect of external factors such as skill differences and training effect, and

strengthen the external validity regarding skilled participants (the target population).

Since the task was demanding, only 5 Participants carried out all 5 sessions, the other 4

Participants completed only three sessions.

2.3 Tasks

The participant performed a main and a secondary task simultaneously. The main task was

tracking a 2D moving target on the screen, with a joystick controlled crosshair. The 12

target movement conditions were composed of 3 target maneuvering profiles and 4

constant-velocity levels.

The target velocities were: (pixel units- 50/sec (1.5°/sec), 80/sec (2.4°/sec), 110/sec

(3.3°/sec), and 140/sec (4.2°/sec)). The three manoeuvring types were: P – straight lines

and sharp turns, M – rounded lines and turns and D – the same as P plus 1 second target

disappearances.

The secondary task was a compensatory-tracking task: a small unstable rectangle

inclined to break out of a bigger rectangle, and could be returned to center by joystick

trigger-press. This task was displayed in the lower third of screen area.


Fig. 1: Schematic study screen-view: Main task (track a moving target joystick controlled crosshair), red disk and black crosshair, and secondary (return the inner rectangle to center by joystick trigger-press) tasks.

2.4 Apparatus and Measures

2.4.1 hardware

The display screen was an Alienware OptX AW2210, 21.5-Inch monitor, 1920x1080

resolution and 120Hz. refresh rate. The joystick controller was the Saitek X52-pro. The

physiology measurement systems (GSR and FSR sensors, controllers, amplifiers and

operation software) were manufactured by “Atlas Engineering”.

2.4.2 software

The operating system we used is windows 7 SP1 x 64 bits. We engaged the development

environment C# .net 4.0 and XNA 4.0 build x86 (32 bit).

2.4.3 measures

The main performance measure was percent of time the crosshair was within the target

area.

2.5 Procedure

Participant attended the laboratory on two different days, for test sessions which lasted

about 90 minutes each. During the first session Participants received instructions and were

asked to read and sign their consent for participation.


The Participants were informed that their experimental task performance level would

influence their reward (in form of bonus points at an academic course grade). Following

the explanation, Participants went through several visual-tests to ensure proper vision and

filled in a short questionnaire, regarding personal details

The experimenter demonstrated the experimental task and then the Participants began a

training phase including several levels of difficulty. Initially they completed the easiest

level. After meeting a pre-defined criterion they progressed to the next level and so on, up

to meeting the final level criterion of performance. Participants who completed 20 training

trials without meeting the performance criterion (the crosshair within target area at least

70% of the time) were ruled out (see section 2.2- participants, for more details) and did not

proceed to the experimental phase.

After the training phase, the Participants completed 3 to 5 experimental sessions, while

monitored by the physiologic sensors: GSR and FSR. All the sessions consisted of 12

trials, 45 seconds long each. Every session included all possible combinations of four

target velocities and three target manoeuvres, randomly ordered. After each session

Participants were informed of their tracking performance.

Fig. 2: view of experimental setup.

3 Results

3.1 Statistical Analyses


To test our Hypotheses we performed three major analyses. First, we conducted a bivariate

regression analysis of FSR vs. GSR to examine the linear correlation between the two

variables (hypothesis 1).

Second, we conducted a two factors anova (4 target velocities X 3 target maneuvers)

on participants’ performance, to examine weather complex target manoeuvres and higher

target velocities are more difficult (hypothesis 2).

Third, we conducted a two factors anova (4 target velocities X 3 target maneuvers) on

participants’ FSR level, to examine whether difficult task conditions induce higher FSR

measurement values (hypothesis 3).

3.2 Regression Analysis of FSR vs. GSR (hypothesis 1)

We performed two bivariate regressions analyses of FSR vs. GSR, in order to investigate

the relationship of FSR and GSR throughout the experiment. First we conducted a

regression analysis with all 9 participants who started the experiment (4 participants didn’t

finish the experiment due to high task demands, (see section 2.2, participants)). Thereafter

we conducted a regression analysis with only 5 participants who completed all 5

experimental sessions.

Unstandardized Coefficient, B

B standard error

standardized Coefficient,

Beta

t

Bivariante Reggression with 9 participants (no performance criteron)

0.44 0.06 0.45 7.20***

Bivariante Reggression with 5 participants (4 filltered due to performance criteria)

0.85 0.08 0.75 11.31***

p<0.001***


Table 1: Bivariante regression coefficients of FSR vs. GSR, for 9 participants (including 4 participants who didn’t finish the experiment due to high task demands) and for 5 participants who completed all 5 experimental sessions.

Regression results for 9 participants show that there is a good linear correlation between

participants’ measures of FSR and GSR (β=0.44, R²=0.20, F(1,210)=51.84, p<0.001).

Thus, the higher the FSR measurement level, the higher the GSR level (see table 1 for

regression coefficients).

Regression results for 5 participants show a very strong linear correlation between

participants’ measures of FSR and GSR (β=0.85, R²=0.56, F(1,102)= 127.91, p<0.001).

Thus, the higher the FSR measurement level, the higher the GSR level (see table 1 for

regression coefficients). According to these results, participants’ FSR values explain 56%

of participants’ GSR values, through the following prediction equation:

GSR= (-148.17) + 0.85*FSR (see figure 3).

Fig. 3: Regression line of FSR vs. GSR for 5 participants who completed all 5 experimental


sessions.

3.3 factorial anova of target velocities and target maneuvers on participants’

performance (hypothesis 2)

In order to examine whether higher target velocities and complex target maneuvers elicit

lower participants’ performance, we conducted a two factors anova analysis. The following

graph reflects the participants’ performance level (percent of time the crosshair was within

the target area) for each combination of the 12 task conditions (4 target velocities X 3

target maneuvers).

Fig. 4: The graph displays the mean performance (percent of time the crosshair was within the target area) of each combination of the 12 conditions- 3 target maneuvers (P – straight lines and sharp turns, M – rounded lines and turns and D – the same as P plus random one second target disappearances) by 4 target velocities (pixel units- 50/sec, 80/sec, 110/sec, and 140/sec).


Results show a strong and significant main effect of target velocity on performance, (F

(3, 213) = 209.340, p < .001). The multiple comparisons post hoc test revealed that the

participants performance level was significantly different (p < .001) for each of the 4 target

velocities (pixel units- 50/sec (1.5°/sec), 80/sec (2.4°/sec), 110/sec (3.3°/sec), and 140/sec

(4.2°/sec)), so that the higher the velocity the lower the performance.

Results show another significant main effect of target maneuver (P – straight lines and

sharp turns, M – rounded lines and turns and D – the same as P plus one second target

disappearances) on performance (F (2, 213) = 11.472, p < .001). The multiple comparisons

post hoc test revealed that: participants performance was significantly lower on the D

maneuver, compared to the P maneuver (p< .005) and compared to the D maneuver

(p< .005) (see table 2 for means and standard deviations). The participants’ performance

on the M maneuver condition was only marginally significantly lower than the P

maneuvers (p = .068).

Furthermore, Results show a significant interaction effect between target velocity and

target maneuver, on performance, (F (6, 213) = 4.119, p < .001). This indicates that

different target velocities affected the participants’ performances differently in each of the

three target maneuvers (and vice versa). Specifically, the M maneuver appeared to be

easier (higher performance) at the low target velocity of 50 (M = 86.54, SD = 7.973),

compared to the P maneuver (M = 79.38, SD = 8.077). However, the M maneuver

appeared to be harder (lower performance) at the high target velocity of 140 (M = 34.01,

SD = 9.331), compared to the P maneuver (M = 37.59, SD = 9.230).

Mean performance (% of time the crosshair was

within the target area)

Standard deviation

Target Velocity Target Maneuver

50

D 70.82% 10.10%

M 86.54% 7.97%

P 79.38% 8.08%

Total 78.77% 10.82%


80

D 53.85% 10.76%

M 58.96% 14.89%

P 60.57% 11.13%

Total 57.91% 12.51%

110

D 40.98% 10.34%

M 36.15% 11.67%

P 49.28% 7.96%

Total 42.01% 11.34%

140

D 29.51% 9.16%

M 34.01% 9.33%

P 37.59% 9.23%

Total 33.70% 9.67%

Total D 48.72% 18.43%

M 53.48% 23.89%

P 56.55% 17.84%

Total 52.92% 20.41%

Table 1: Mean Participants’performance (percent of time the crosshair was within the target area) of the 12 task condition combinations - each of the 4 target velocities (pixel units- 50/sec, 80/sec, 110/sec, and 140/sec) are divided into 3 target maneuvers (P – straight lines and sharp turns, M – rounded lines and turns and D – the same as P plus random one second target disappearances).

3.4 factorial anova of target velocities and target maneuvers on participants’ FSR

levels (hypothesis 3)

In order to examine whether higher target velocities and complex target maneuvers

produce higher levels of FSR values, we conducted a two factors anova analysis.

The results show a significant main effect for target velocity on FSR (F (3, 193) = 2.57,

p = .05). The multiple comparison post hoc test revealed that the highest target velocity

(140 pixel/sec) evoke significantly (p < .05) higher FSR measurements levels, compared to


the two lowest target velocities (50 pixel/sec and 80 pixel/sec). The following graph

display the FSR measurement levels in the different target velocities.

Fig. 5: FSR by target velocities (levels of difficulty).

Results did not show significant main effect for target maneuver and did not show

significant interaction effect.

4 Discussion

The described results support our Hypotheses.


In accordance with the first hypothesis, the findings indicate that there is a relationship

between the GSR and the FSR measures. This implies FSR as a valid stress measurement

tool.

The findings also supported our second hypothesis. We found that the experimental

manipulation proved effective. Complex target manoeuvres and higher target velocities

produced lower performance levels (participants’ performance were much more affected

by target velocity, compared with target manoeuvre). Thus, Complex target manoeuvres

and higher target velocities proved as more difficult for the participants. Based on the

cognitive stress approach (Lazarus, 1966; Lazarus & Launier, 1978; Staal, 2004), the

contingency of performance levels with course credits elicited a stress factor, providing an

additional stress value to the various task difficulty levels, beyond their workload values.

These finding are important for our third hypothesis.

Finally, the findings partially support our third hypotheses. The results show that higher

target velocities elicited higher FSR values. Since, as previously claimed, higher velocity

levels incorporate workload stress and psychological stress features, it is reasonable to

conclude that the higher FSR values reflects higher stress levels. Contrary to the

“maneuver section” of our third hypothesis, the results did not show that complex target

maneuver elicited higher FSR values. It is possible that since participants’ performance

were much more affected by target velocity, only the “velocity section” of our third

hypothesis was supported, and not the “maneuver section".

In conclusion, our results support the feasibility of the FSR measure (Grip Force) as a

valid and reliable stress measure in psychomotor tasks. This newly emerged tool provides

stress measurement without the awareness of the measured person.

Although this newly discovered indicator seems promising, more research is required

before it could be used operationally. Subsequent studies should examine the FSR real-

time measurement aspects and how to establish measurement baseline for different

individuals. There is a necessity to map this tool's limitations and boundaries in order to

achieve optimal measurement.


Acknowledgement

This research was supported by the Israeli Administration for the Development of Weapons and Technological Infrastructure (Maf'at) and the Israeli Air Force. We would like to thank personally to Colonel Michal Hovav, head of human factors engineering section in Maf'at and to Major Avshalom Gil-ad, head of human factors engineering department in the Israeli air force, for their help and support. We would like to thank Lior Lahav, for building and managing the programming and technologic aspects of the research and to Namik Benyminov for his continuance support in the maintenance of these aspects.

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M. Wagner1, Y · Web viewThis study was motivated by two elements, first is real life pilot report, the second is more scientific. Important but non-scientific source for the “Grip