Stress Detection Using Smartphone and Wearable Devices: A Review

  • Tejaswini Panure
  • Shilpa Sonawani

Abstract

Stress is the mental condition of the human body that
causes it’s dis-functioning. It affects adversely on body parts
resulting in health disorders. Traditional method of stress
detection includes lab tests done by doctor. Besides traditional
techniques, sensors are used to measure physiological signals,
as these signals make it easy to detect stress. Based on
techniques of data collection, this paper is divided into two
types, one for In-lab experiment, in which participants wear
various sensors on their body which is invasive for real time
application while in second, data was collected from sensors
which are already available in the handy devices of participant
such as smartphone, wearable devices etc. Different types of
sensors and their uses are explained in this paper. Automatic
real time stress detection systems can be developed. This paper
lists various algorithms used to gain more accuracy in detecting
stress. This paper is helpful for the fellow researchers who will
be working on automatic stress detection. Various studies in
this domain have been reviewed and this is a primary effort in
summarizing the highlights of the previous research done in
stress detection domain.

Keywords: Stress detection, Physiological signals, Fitbit, E4

References

[1]Mitrpanont J., Phandhu-fung, J., Klubdee, N., Ratanalaor, S., Pratiphakorn, P.,
Damrongvanakul, K., Piyawat Chuanvaree, P. and Mitrpanont, T. (2019).
iCare-Stress: Caring system for stress - IEEE Conference Publication.
[online] Ieeexplore.ieee.org. Available at:
https://ieeexplore.ieee.org/document/8075319/ [Accessed 7 Jan. 2019].
[2]Akmandor, A. and Jha, N. (2017). Keep the Stress Away with SoDA: Stress
Detection and Alleviation System. IEEE Transactions on Multi- Scale
Computing Systems, 3(4), pp.269-282.
[3]de Santos Sierra, A., Sanchez Avila, C., Guerra Casanova, J. and Bailador del
Pozo, G. (2011). A Stress-Detection System Based on
Physiological Signals and Fuzzy Logic. IEEE Transactions on Industrial
Electronics, 58(10), pp.4857-4865.
[4] Kostopoulos, P., Kyritsis, A., Deriaz, M. and Konstantas, D. (2016). Stress
Detection Using Smart Phone Data. Lecture Notes of the Institute for
Computer Sciences, Social Informatics and Telecommunications
Engineering, pp.340-351.
[5] Greene, S., Thapliyal, H. and Caban-Holt, A. (2016). A Survey of Affective
Computing for Stress Detection: Evaluating technologies in stress detection
for better health. IEEE Consumer Electronics Magazine, 5(4), pp.44-56.
[6] Liao, C., Chen, R. and Tai, S. (2018). Emotion stress detection using EEG
signal and deep learning technologies. 2018 IEEE International Conference
on Applied System Invention (ICASI).
[7] Ramteke, R. and Thool, V. (2017). Stress detection of students at academic
level from heart rate variability. 2017 International Conference on Energy,
Communication, Data Analytics and Soft Computing (ICECDS).
[8] Gjoreski, M., Luštrek, M., Gams, M. and Gjoreski, H. (2017). Monitoring
stress with a wrist device using context. Journal of Biomedical Informatics,
73, pp.159-170.
[9] Thapliyal, H., Khalus, V. and Labrado, C. (2017). Stress Detection and
Management: A Survey of Wearable Smart Health Devices. IEEE
Consumer Electronics Magazine, 6(4), pp.64-69.
[10] Ollander, S., Godin, C., Campagne, A. and Charbonnier, S. (2016). A
comparison of wearable and stationary sensors for stress detection. 2016
IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[11] Gjoreski, M., Gjoreski, H., Lutrek, M. and Gams, M. (2015). Automatic
Detection of Perceived Stress in Campus Students Using Smartphones.
2015 International Conference on Intelligent Environments.
[12] Garcia-Ceja, E., Osmani, V. and Mayora, O. (2016). Automatic Stress
Detection in Working Environments From Smartphones’ Accelerometer
Data: A First Step. IEEE Journal of Biomedical and Health Informatics,
20(4), pp.1053-1060.
[13] Vaizman, Y., Ellis, K. and Lanckriet, G. (2017). Recognizing Detailed
Human Context in the Wild from Smartphones and Smartwatches. IEEE
Pervasive Computing, 16(4), pp.62-74.
[14] Zhang, X., Li, W., Chen, X. and Lu, S. (2018). MoodExplorer. Proceedings
of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,
1(4), pp.1-30.
[15] Lu, J., Bi, J., Shang, C., Yue, C., Morillo, R., Ware, S., Kamath, J., Bamis,
A., Russell, A. and Wang, B. (2018). Joint Modeling of Heterogeneous
Sensing Data for Depression Assessment via Multi-task Learning.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous
Technologies, 2(1), pp.1-21.
[16] Xia, L., Malik, A. and Subhani, A. (2018). A physiological signal- based
method for early mental-stress detection. Biomedical Signal Processing and
Control, 46, pp.18-32.
[17] Sioni, R. and Chittaro, L. (2015). Stress Detection Using Physiological
Sensors. Computer, 48(10), pp.26-33.
[18] Sriramprakash, S., Prasanna, V. and Murthy, O. (2017). Stress Detection in
Working People. Procedia Computer Science, 115, pp.359- 366.
[19] Smith, A. and Thomas, A. (2018). Reducing the Consequences of Acute
Stress on Memory Retrieval. Journal of Applied Research in Memory and
Cognition, 7(2), pp.219-229.
[20] Koussaifi, M., Habib, C. and Makhoul, A. (2018). Real-time stress
evaluation using wireless body sensor networks. 2018 Wireless Days(WD).
[21] Liu, Y. and Du, S. (2018). Psychological stress level detection based on
electrodermal activity. Behavioural Brain Research, 341, pp.50-53.
[22] de Santos Sierra, A., Sanchez Avila, C., Guerra Casanova, J. and Bailador
del Pozo, G. (2011). A Stress-Detection System Based on Physiological
Signals and Fuzzy Logic. IEEE Transactions on Industrial Electronics,
58(10), pp.4857-4865.
[23] Han, L., Zhang, Q., Chen, X., Zhan, Q., Yang, T. and Zhao, Z. (2017).
Detecting work-related stress with a wearable device. Computers in
Industry, 90, pp.42-49.
[24] Zhang, X., Lyu, Y., Luo, X., Zhang, J., Yu, C., Yin, H. and Shi, Y. (2018).
Touch Sense. Proceedings of the ACM on Interactive, Mobile, Wearable
and Ubiquitous Technologies, 2(2), pp.1-18.
[25] “Stress Detection Using Physiological Sensors”, Riccardo Sioni and Luca
Chittaro, Volume 48, Issue 10, October 2015.
[26] Kalas, M. and Momin, D. (2019). Stress detection and reduction using EEG
signals - IEEE Conference Publication. [online] Ieeexplore.ieee.org.
Available at: https://ieeexplore.ieee.org/document/7755604 [Accessed 8
Jan. 2019]. [27] Aigrain, J., Spodenkiewicz, M., Dubuiss, S., Detyniecki,
M., Cohen, D. and Chetouani, M. (2018). Multimodal Stress Detection
from Multiple Assessments. IEEE Transactions on Affective Computing,
9(4), pp.491-506.
[28] VAIZMAN,, Y., WEIBEL,, N. and LANCKRIET,, G. (2019). Context
Recognition In-the-Wild: Unified Model for Multi-Modal Sensors and
Multi-Label Classification. [online] Extrasensory.ucsd.edu. Available at:
http://extrasensory.ucsd.edu/papers/vaizman2017b_imwutAcceptedVersion
.pdf [Accessed 8 Jan. 2019].
[29] Fernandes, A., Helawar, R., Lokesh, R., Tari, T. and V. Shahapurkar,
A. (2019). Determination of stress using Blood Pressure and Galvanic Skin
Response - IEEE Conference Publication. [online] Ieeexplore.ieee.org.
Available at: https://ieeexplore.ieee.org/document/7062747 [Accessed 8
Jan. 2019].
[30] Marina Marcus, M. Taghi Yasamy, Mark van Ommeren, and Dan Chisholm,
Shekhar Saxena. (2012). Depression: A Global Public Health Concern |
HESP News Briefing – RSS feeds. [Online]. Available:
http://hespnews.org /2012 /10/ 05/depression-a-global-public-healthconcern/.
[31] http://www.who.int/mental_health/management/depression/who_paper
_depression_wfmh_2012.pdf
[32] https://www.who.int/whr/2001/media_c entre/press_release/en/
[33] Ranabir, S. and Reetu, K. (2011). Stress and hormones. Indian Journal of
Endocrinology and Metabolism, 15(1), p.18.
[34]https://www.uml.edu/docs/IntroductionToJobStress_tcm18-42460.pdf
Statistics
0 Views | 0 Downloads
How to Cite
Panure, T., & Sonawani, S. (2019). Stress Detection Using Smartphone and Wearable Devices: A Review. Asian Journal For Convergence In Technology (AJCT). Retrieved from http://asianssr.org/index.php/ajct/article/view/722
Section
Article