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 GripForce 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:
1
Stress, Grip-Force, GSR, Psychomotor, Tracking.
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from unpublished military aviation medicine studies, and reported pilot experience.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
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
Wagner, Sahar, Elbaum & Berliner
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
Wagner, Sahar, Elbaum & Berliner
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).
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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.
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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 2Dmanoeuvring 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).
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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”.
Wagner, Sahar, Elbaum & Berliner
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.
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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.
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Unstandardized
B standard
standardized
Coefficient, B
error
Coefficient,
t
Beta
Bivariante Reggression with 9
participants
0.44
0.06
0.45
7.20***
0.85
0.08
0.75
11.31***
(no performance criteron)
Bivariante Reggression with 5
participants
(4 filltered due to performance
criteria)
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
GSR= (-148.17) + 0.85*FSR (see figure 3).
the
following
prediction
equation:
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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).
Wagner, Sahar, Elbaum & Berliner
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
Wagner, Sahar, Elbaum & Berliner
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).
Target Velocity
Standard deviation
Target Maneuver
Mean performance
(% of time the crosshair was
within the target area)
D
70.82%
10.10%
M
86.54%
7.97%
P
79.38%
8.08%
50
Total
80
78.77%
D
53.85%
10.76%
M
58.96%
14.89%
P
60.57%
11.13%
Total
110
57.91%
40.98%
10.34%
M
36.15%
11.67%
P
49.28%
7.96%
42.01%
11.34%
D
29.51%
9.16%
M
34.01%
9.33%
P
37.59%
9.23%
Total
Total
12.51%
D
Total
140
10.82%
33.70%
9.67%
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).
Wagner, Sahar, Elbaum & Berliner
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.
Wagner, Sahar, Elbaum & Berliner
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 realtime 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.
Wagner, Sahar, Elbaum & Berliner
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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