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Psychology, 2017, 8, 978-986
http://www.scirp.org/journal/psych
ISSN Online: 2152-7199
ISSN Print: 2152-7180
DOI: 10.4236/psych.2017.87064 May 19, 2017
Continuous Estimation of Stress Using
Physiological Signals during a Car Race
Wen Wen1, Daisuke Tomoi1, Hiroshi Yamakawa1, Shunsuke Hamasaki1, Kaoru Takakusaki2,
Qi An1, Yusuke Tamura1, Atsushi Yamashita1, Hajime Asama1
1Department of Precision Engineering, University of Tokyo, Tokyo, Japan
2The Center for Brain Function and Medical Engineering, Asahikawa Medical College, Asahikawa, Japan
Abstract
Mental stress refers to the feeling of strain
and anxiety caused by internal
and/or external factors. Stress may have both positive and negative influences
on cognitive performance. For example, small amounts of stress may improve
concentration, athletic performance, and reaction speed. However, exces
sive
stress may harm health, and disturb concentration and motor control. Fu
r-
thermore, increase in short-term stress has a considerable effect on physi
o-
logical processes such as heart rate and sweating. However, to our knowledge,
so far no study has estima
ted mental stress through continuous monitoring of
physiological signals under a situation with various stressors. Therefore, it
remains unclear how different physiological signals correlate with each other
and how they change continuously in response to t
he changes in stressors.
Here, we measured heart rate variability, galvanic skin response, and activity
of the masseter muscle in a professional racer during a real car race. Car ra
c-
ing is one of the most dangerous sports, and the competition with other ra
cers
and physical discomfort during the race induce great short-
term stresses. We
used factor analysis to examine the relation between the three types of phys
i-
ological signals, and clarified the events associated with stress (e.g., acceler
a-
tion, competing
car in vision, overtaking). The results showed that the heart
rate variability and galvanic skin response correlated with each other, and
were associated with the event of competition, which brought great mental
stress. In contrast, activity of the massete
r muscle did not correlate with the
other two physiological signals, but was associated with the events of acceler
a-
tion or deceleration, which brought great physical discomfort. We concluded
that heart rate variability and galvanic skin response reflect me
ntal stress,
which is associated with an internal desire to win and is triggered by changes
in the external world (e.g., the appearance of competing cars). In contrast,
masseter muscle activity reflects the endurance of body discomfort, which is
not linked to any internal state. Our results indicated that internal and exte
r-
nal stressors may be dissociated from each other, and may affect different or-
How to cite this paper:
Wen, W.,
Tomoi,
D
., Yamakawa, H., Hamasaki, S., Takaku-
saki, K
., An, Q., Tamura, Y., Yamashita, A.,
&
Asama, H. (2017). Continuous Estima-
tion of Stress Using Physiological Signals
during a Car Race
.
Psychology, 8,
978-986.
https://doi.org/10.4236/psych.2017.87064
Received:
April 5, 2017
Accepted:
May 16, 2017
Published:
May 19, 2017
Copyright © 201
7 by authors and
Scientific
Research Publishing Inc.
This work is licensed under
the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
W. Wen et al.
979
gans and physiological receptors.
Keywords
Stress, Physiological Signals, Heart Rate, Galvanic Skin Response,
Masseter Muscle Activity, Factor Analysis
1. Introduction
In psychology, stress refers to the mental state of feeling strain and anxiety, and
is usually caused by changes in the environment or internal desire. Small
amounts of stress could be helpful to improve concentration and athletic per-
formance. However, excessive or chronic stress usually causes depression, harms
health, and results in errors in decision-making or motor control. There are
many types of stressors, such as crises, threats, daily annoyances, and life events.
In the present study, we focused on short-term stress, which arises from rapid
environmental changes such as threat, cognitive load, and emotions.
Stress is a significant subjective experience that is difficult to estimate quanti-
tatively from a subjective perspective. An individual may be able to report
whether he/she is undergoing great or small stress, but is not able to quantify it,
or to compare the amount of stress between different stressors. However, pre-
vious studies have attempted to measure physiological reactions such as heart-
beat, sweating, muscle activities, and blood pressure in stressful situations as an
approach to detect or estimate the amount of stress (Hidaka, Yanagi, & Takada,
2004b; Hjortskov et al., 2004; Kurniawan, Maslov, & Pechenizkiy, 2013; Shi et al.,
2007; Thayer, Ahs, Fredrikson, Sollers, & Wager, 2012; Yemm, 1969). Previous
studies have revealed useful links between some physiological signals and stress.
However, none of these studies has examined physiological signals in a situation
with multiple and dynamically changing stressors.
In the present study, we focused on the situation of a real car race. We conti-
nuously measured three types of physiological signals in a professional driver
during a real race. Car racing is one of the most dangerous sports, and requires
continuous and rapid decisions during a relatively long duration. Both the com-
petition and physical discomfort could be stressors, and multiple stressors may
change rapidly. The present study aimed to clarify the relations between multiple
physiological signals (i.e. heartbeat, sweating, and masseter muscle activity) in
such a situation, and to elucidate the relation between physiological signals and
different stressors. Our present findings could not only provide useful estima-
tion of stress during the specific situation of car race but also contribute to the
understanding of the subjective experience of stress.
2. Methods
2.1. Participant and Experimental Environment
The present study focused on within-individual correlation in a highly specific
W. Wen et al.
980
situation. Therefore, we invited a professional race driver (male, age 27 years) to
take part in a real car race, and measured physiological signals during the race.
The study was approved by the ethics committee of the Faculty of Engineering at
the University of Tokyo, and written informed consent was obtained from the
participant prior to the experiment.
The experiment was performed during a practice session of a race, in which all
racers took part in order to get used to the racing course in advance of the actual
race. The racing condition was the same as the actual one, in which the racers
were free to overtake other cars. Figure 1 illustrates the map of the racing course
of the present study. The racing course contained 12 corners and two slopes, and
therefore required highly precise control of speed and direction.
2.2. Equipment
The present study measured the participant’s heart rate variability (HRV), gal-
vanic skin response (GSR), and muscle activity of the masseter (MAM). All three
physiobiological signals have been reported to be associated with stress (Hidaka
et al., 2004b; Hjortskov et al., 2004; Kurniawan et al., 2013; Shi et al., 2007;
Thayer et al., 2012; Yemm, 1969). However, previous studies used different sig-
nals in different stressful conditions, and the relation between these signals and
different stressors remains unknown.
The three physiobiological signals were measured using galvanic skin response
(GSR) sensors (DL-340, S&ME), heartbeat (electrocardiogram [ECG]) sensors
(DL-310, S&ME), and electromyography (EMG) sensors (DL-41, S&ME), and
were recorded in a data logger (DL-3100, S&ME), which was attached to the par-
ticipant’s waist. The sensors were attached to the participant’s skin using medi-
cal tape, and were referenced to the right wrist. Figure 2 illustrates the measur-
ing positions of all three signals.
2.3. Preprocessing of the Physiobiological Signals
All three physiobiological signals were recorded at a frequency of 1000 Hz. The
heart rate variability was recorded from ECG electrodes, which were placed at
Figure 1. Racing course used in the experiment.
W. Wen et al.
981
Figure 2. Positions of the sensors. GSR, galvanic skin response; EMG,
electromyogram (to measure muscle activities); ECG, electrocardiogram
(to measure heart rate).
the distal part of the sternum and at the sixth rib in the left axilla. An offline
band filter of 0.16 - 500 Hz was applied to the ECG signals. From the ECG, we
calculated the intervals between peaks (R-R intervals), standard deviation of the
R-R intervals (SDNN), and root mean squared successive differences of R-R in-
tervals (RMSSD). The ratio of SDNN to RMSSD was used as the index of HRV.
The GSR was recorded using GSR sensors placed at the left and right mastoids.
Most previous studies placed the GSR sensors on the hand or foot (e.g., Healey &
Picard, 2000; Nourbakhsh, Wang, Chen, & Calvo, 2012). However, in the real
car racing, this may disturb the driver’s sensation on hands and feet, and has the
risk of causing fatal mistakes. Therefore, we avoided these positions and chose
mastoids as the measuring site instead. Finally, the MAM was recorded from two
pairs of EMG sensors placed at the left and right masseters. Activities of facial
muscles are useful objective indices of emotion (Cacioppo, Petty, Losch, & Kim,
1986), and jaw muscles in particular are susceptible to mental stress (Hidaka,
Yanagi, & Takada, 2004a; Hidaka et al., 2004b). However, unlike HRV and GSR,
masseters can be activated by consciousness or habit; therefore, the role of the
MAM sensor as an anchor of stress may be different from the former two sen-
sors.
2.4. Procedure
Physiological signals were recorded during the practice session of a real race.
The participant raced over the entire racing course six laps without break. The
race was recorded with a video camera that was installed in the racing car and
recorded the view of the driver. The position of the participant’s racing car was
recorded using global positioning system (GPS) sensors attached to the car.
3. Results
According to the results of an interview with the participant, the first and second
W. Wen et al.
982
laps were considered as a warming up phase and were therefore excluded from
the analysis. Only the results from the third and sixth laps were included in the
analysis. We used factor analysis to examine the relations between the three
physiological signals. The analysis revealed two factors: the first factor 1) influ-
enced both HRV and GSR, while the second factor 2) influenced MAM. Table 1
summarizes the coefficients of the two factors. The factor coefficient shows the
extent to which a factor influences a variable.
In order to determine the events that were associated with the observed activi-
ties of the two factors, we abstracted two events from the video and GPS records
of the car: 1) competition and 2) acceleration/deceleration. The event of compe-
tition refers to the time when another car appeared in the visual field of the par-
ticipant (Figure 3). The event of acceleration/deceleration was calculated from
the GPS recordings. Figure 4 illustrates the plots of the factor scores in function
of time. The events of competition greatly overlaid with the high scores of factor
1 (i.e. HRV and GSR), while the events of acceleration/deceleration greatly over-
laid with high scores of factor 2 (i.e. MAM). Furthermore, a qualitative analysis
showed that 92% of the events of competition consisted of high scores of factor 1
(>2.5 standard deviation), but only 37% contained high scores of factor 2. In
contrast, 54% of the events of acceleration/deceleration contained high scores of
factor 1, while 63% contained high scores of factor 2. In summary, the results
showed that factor 1 influenced HRV and GSR and was associated with the event
of competition, while factor 2 influenced MAM and was associated with the
event of acceleration/deceleration.
Table 1. Coefficients of the two factors.
Factor 1 Factor 2
HRV 0.362 0.154
GSR 0.346 −0.207
MAM 0.015 0.355
Figure 3. An example of a competition event.
W. Wen et al.
983
Figure 4. The scores of factor 1 (a) and 2 (b) plotted in function of time, and the overlay with the events of competition and acce-
leration/deceleration.
4. Discussion
The present study measured three different physiological signals, all of which are
considered to reflect mental stress, in a professional driver during a real car race.
We examined the relations between the three physiological signals using factor
analysis, and the association with significant events during the race. The results
showed that HRV and GSR were highly correlated with each other, and were as-
sociated with the events of competition. On the other hand, MAM was not cor-
related with the other two physiological signals, and was associated with the
W. Wen et al.
984
events of acceleration/deceleration. Our present findings suggested that, al-
though all the three physiological signals are somehow affected by stress, they
probably reflect different types of stressors. In particular, HRV and GSR are
probably closer to mental stress, which is mainly the result of internal state of
desire and triggered by changes in the external world. On the other hand, MAM
may be triggered by physical discomfort, which could also result in stress, but is
more bottom-up and does not require an internal state.
To our knowledge, the present study is the first that measured multiple physi-
ological signals from an extremely dangerous and stressful situation in real life.
Most previous studies examined stress under experimental conditions, and the
“real” stress may have not been accurately simulated. The stressors examined in
previous studies were more controlled and had less variability. For example,
cognitive load (Hjortskov et al., 2004; Lattimore & Maxwell, 2004; Shi et al.,
2007), unfriendly attitude (Hjortskov et al., 2004; Montirosso, Provenzi, Calci-
olari, Borgatti, & Group, 2011), and emotion (Kim, Bang, & Kim, 2004) are the
most common stressors used in experimental tasks. However, none of them pro-
vides continuous and diverse changes in stress, and therefore could not reveal
the relationship between different physiological signals during such changes.
During the car race, the driver was not only under high cognitive load but also
faced a life-threatening situation, and was inspired by the desire to win at every
moment. In addition, because of the high velocity and g-force during accelera-
tion/deceleration, the driver also suffered from stress resulting from physical
discomfort. Therefore, the situation of car racing provided an ideal environment
of multiple sources of stress and continuous changes, allowing us to examine the
continuous changes in physiological reactions that are associated with different
stressors.
The most important finding of our results was that stressors associated with
an internal state, such as the desire to win, and that are only linked to external
stimuli may result in different physiological reactions. During the race, accelera-
tion/deceleration brought discomfort to the body because of large g-force, and
probably resulted in a stressful situation. However, both HRV and GSR were not
sensitive to such condition, indicating that, although the participant might be
suffering from a physical discomfort, his mental stress associated with the inter-
val state did not change significantly. This does not necessarily mean that the
mental stresses resulted from internal and external states have to be dissociated.
Instead, in most occasions, they might be linked to each other, but learning (e.g.,
long-term exposure to a specific physical discomfort) may be able to separate
them, thus controlling mental stress from the bottom-up physical stimuli.
5. Conclusion
The present study showed that competition and physical discomfort may result
in different physiological reactions during a real car race. In particular, we found
that the event of competition triggered an increase in HRV and GSR, while the
acceleration/deceleration event triggered MAM. The results indicated that dif-
W. Wen et al.
985
ferent stressors, associated with internal or external states or stimuli, may result
in disparate physiological reactions.
Acknowledgements
This work was supported in part by JSPS KAKENHI (Grant Numbers 26120005).
References
Cacioppo, J. T., Petty, R. E., Losch, M. E., & Kim, H. S. (1986). Electromyographic Activ-
ity over Facial Muscle Regions Can Differentiate the Valence and Intensity of Affective
Reactions.
Journal of Personality and Social Psychology, 50,
260-268.
https://doi.org/10.1037/0022-3514.50.2.260
Healey, J., & Picard, R. (2000). SmartCar: Detecting Driver Stress.
Proceedings 15th In-
ternational Conference on Pattern Recognition. ICPR-2000, 4,
218-221.
https://doi.org/10.1109/ICPR.2000.902898
Hidaka, O., Yanagi, M., & Takada, K. (2004a). Changes in Masseteric Hemodynamics
Time-Related to Mental Stress
. Journal of Dental Research, 83,
185-190.
https://doi.org/10.1177/154405910408300220
Hidaka, O., Yanagi, M., & Takada, K. (2004b). Mental Stress-Induced Physiological
Changes in the Human Masseter Muscle.
Journal of Dental Research, 83,
227-231.
https://doi.org/10.1177/154405910408300308
Hjortskov, N., Rissén, D., Blangsted, A. K., Fallentin, N., Lundberg, U., & Søgaard, K.
(2004). The Effect of Mental Stress on Heart Rate Variability and Blood Pressure dur-
ing Computer Work.
European Journal of Applied Physiology, 92,
84-89.
https://doi.org/10.1007/s00421-004-1055-z
Kim, K. H., Bang, S. W., & Kim, S. R. (2004). Emotion Recognition System Using
Short-Term Monitoring of Physiological Signals.
Medical and Biological Engineering
and Computing, 42,
419-427. https://doi.org/10.1007/BF02344719
Kurniawan, H., Maslov, A. V., & Pechenizkiy, M. (2013). Stress Detection from Speech
and Galvanic Skin Response Signals.
Proceedings of CBMS 2013—26th IEEE Interna-
tional Symposium on Computer-Based Medical Systems,
209-214.
https://doi.org/10.1109/cbms.2013.6627790
Lattimore, P., & Maxwell, L. (2004). Cognitive Load, Stress, and Disinhibited Eating
. Eat-
ing Behaviors, 5,
315-324. https://doi.org/10.1016/j.eatbeh.2004.04.009
Montirosso, R., Provenzi, L., Calciolari, G., Borgatti, R., & Group, N.-A. S. (2011). Mea-
suring Maternal Stress and Perceived Support in 25 Italian NICUs.
Acta Paediatrica,
101,
136-142. https://doi.org/10.1111/j.1651-2227.2011.02440.x
Nourbakhsh, N., Wang, Y., Chen, F., & Calvo, R. A. (2012). Using Galvanic Skin Re-
sponse for Cognitive Load Measurement in Arithmetic and Reading Tasks.
Proceed-
ings of the 24th Conference on Australian Computer-Human Interaction OzCHI’12,
420-423. https://doi.org/10.1145/2414536.2414602
Shi, Y., Park, T., Ruiz, N., Taib, R., Choi, E. H. C., & Chen, F. (2007). Galvanic Skin Re-
sponse (GSR) as an Index of Cognitive Load.
CHI EA’07 CHI’07 Extended Abstracts
on Human Factors in Computing Systems,
2651-2656.
https://doi.org/10.1145/1240866.1241057
Thayer, J. F., Ahs, F., Fredrikson, M., Sollers, J. J., & Wager, T. D. (2012). A Me-
ta-Analysis of Heart Rate Variability and Neuroimaging Studies: Implications for Heart
Rate Variability as a Marker of Stress and Health.
Neuroscience and Biobehavioral Re-
views, 36,
747-756. https://doi.org/10.1016/j.neubiorev.2011.11.009
W. Wen et al.
986
Yemm, R. (1969). Variations in the Electrical Activity of the Human Masseter Muscle
Occurring in Association with Emotional Stress.
Archives of Oral Biology, 14,
873-878.
https://doi.org/10.1016/0003-9969(69)90265-9
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