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International Conference on Human-Computer Interaction

HCII 2020: Cross-Cultural Design. Applications in Health, Learning, Communication, and Creativity pp 166–175Cite as

A Framework of Real-Time Stress Monitoring and Intervention System

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12193))

Abstract

Stress is a universally experienced phenomenon in modern society. Stress influences the balance of Autonomous Nervous System (ANS). Studies have shown that cumulative stress in daily life can result in cardiovascular diseases and psychological or behavioral disorders, such as depression, and anxiety. Stress has also been investigated as a risk factor for reduced human performances, which in some situation, such as dangerous works or driving a car, may results in negative consequences. Therefore, in order to decrease the negative influence of stress, we proposed a framework for real-time stress measurement, monitoring and intervention. We used physiological responses detected by wearable sensors to measure stress in real time, including heart rate variability (HRV) and electrodermal activity (EDA). This framework can be used to measure and monitor stress in real time and makes it possible to provide corresponding intervention with smartphone for users under different stress levels.

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References

  1. Pacak, K., Palkovits, M.: Stressor specificity of central neuroendocrine responses: implications for stress-related disorders. Endocr. Rev. 22(4), 502–548 (2001)

    Article  Google Scholar 

  2. Fink, G. (ed.): Stress: Concepts, Cognition, Emotion, and Behavior: Handbook of Stress Series, vol. 1. Academic Press, Cambridge (2016)

    Google Scholar 

  3. Kim, D., Seo, Y., Cho, J.: Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 682–685. IEEE (2008)

    Google Scholar 

  4. Schubert, C., Lambertz, M., Nelesen, R.A., et al.: Effects of stress on heart rate complexity—a comparison between short-term and chronic stress. Biol. Psychol. 80(3), 325–332 (2009)

    Article  Google Scholar 

  5. Cohen, S., Kessler, R.C., Gordon, L.U. (eds.): Measuring Stress: A Guide for Health and Social Scientists. Oxford University Press on Demand, Oxford (1997)

    Google Scholar 

  6. Melillo, P., Bracale, M., Pecchia, L.: Nonlinear heart rate variability features for real-life stress detection. Case study: students under stress due to university examination. Biomed. Eng. Online 10(1), 96 (2011)

    Article  Google Scholar 

  7. Milczarek, M., González, E.R., Schneider, E.: OSH in figures: stress at work-facts and figures. Office for Official Publications of the European Communities (2009). https://osha.europa.eu/en/publications/reports/TE-81-08-478-EN-C_OSH_in_figures_stress_at_work

  8. Alberdi, A., Aztiria, A., Basarab, A.: Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. J. Biomed. Inf. 59, 49–75 (2016)

    Article  Google Scholar 

  9. Hassard, J., Teoh, K., Cox, T.: Calculating the cost of work-related stress and psychosocial risks. Technical report, European Agency for Safety and Health at Work, Luxembourg (2014). http://dx.doi.org/10.2802/20493

  10. Jimenez-Molina, A., Retamal, C., Lira, H.: Using psychophysiological sensors to assess mental workload during web browsing. Sensors (Switz.) 18(2), 1–26 (2018). https://doi.org/10.3390/s18020458

    Article  Google Scholar 

  11. Ahujaa, R., Bangab, A.: Mental stress detection in university students using machine learning algorithms. Procedia Comput. Sci. 152, 349–353 (2019)

    Article  Google Scholar 

  12. Cho, Y., Julier, S.J., Bianchi-Berthouze, N.: Instant stress: detection of perceived mental stress through smartphone photoplethysmography and thermal imaging. JMIR Mental Health 6(4), e10140 (2019)

    Article  Google Scholar 

  13. Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., Griss, M.: Activity-aware mental stress detection using physiological sensors. In: Gris, M., Yang, G. (eds.) MobiCASE 2010. LNICST, vol. 76, pp. 282–301. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29336-8_16

    Chapter  Google Scholar 

  14. Patterson, G., Chung, T., Swan, I.: Stress management interventions for police officers and recruits: a meta-analysis. J. Exp. Criminol. 10(4), 487–513 (2014)

    Article  Google Scholar 

  15. Choi, J., Gutierrez-Osuna, R.: Using heart rate monitors to detect mental stress. In: Proceedings of the IEEE International Workshop on Wearable and Implantable Body Sensor Networks, pp. 219–223 (2009)

    Google Scholar 

  16. Boonnithi, S., Phongsuphap, S.: Comparison of heart rate variability measures for mental stress detection. In: Proceedings of the IEEE Computing in Cardiology, pp. 85–88 (2011)

    Google Scholar 

  17. Palanisamy, K., Murugappan, M., Yaacob, S.: Multiple physiological signal-based human stress identification using non-linear classifiers. Elektronika ir elektrotechnika 19(7), 80–85 (2013)

    Article  Google Scholar 

  18. Abouelenien, M., Burzo, M., Mihalcea, R.: Human acute stress detection via integration of physiological signals and thermal imaging. In: Proceedings of the 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments, p. 32 (2016)

    Google Scholar 

  19. Salai, M., Vassányi, I., Kósa, I.: Stress detection using low cost heart rate sensors. J. Healthc. Eng. 2016, 13 (2016)

    Article  Google Scholar 

  20. Subhani, A.R., Mumtaz, W., Saad, M.N.B.M., et al.: Machine learning framework for the detection of mental stress at multiple levels. IEEE Access 5, 13545–13556 (2017)

    Article  Google Scholar 

  21. Zangróniz, R., Martínez-Rodrigo, A., Pastor, J., et al.: Electrodermal activity sensor for classification of calm/distress condition. Sensors 17(10), 2324 (2017)

    Article  Google Scholar 

  22. Sriramprakash, S., Prasanna, V.D., Murthy, O.R.: Stress detection in working people. Procedia Comput. Sci. 115, 359–366 (2017)

    Article  Google Scholar 

  23. Fernández, J.R.M., Anishchenko, L.: Mental stress detection using bioradar respiratory signals. Biomed. Signal Process. Control 43, 244–249 (2018)

    Article  Google Scholar 

  24. Parlak, O., Keene, S.T., Marais, A., et al.: Molecularly selective nanoporous membrane-based wearable organic electrochemical device for noninvasive cortisol sensing. Sci. Adv. 4(7), eaar2904 (2018)

    Article  Google Scholar 

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Acknowledgement

This study is supported by the Ministry of Science and Technology of the People’s Republic of China (project number: 2017YFC0820200) and the Beijing Municipal Natural Science Foundation (project number: 9172008).

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Authors and Affiliations

  1. Department of Industrial Engineering, Tsinghua University, Beijing, 100084, People’s Republic of China

    Peixian Lu, Wei Zhang & Liang Ma

  2. Kingfar Technology, No. 17 Building, No. 18, Anningzhuang East Road, Haidian District, Beijing, China

    Qichao Zhao

Authors
  1. Peixian Lu
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  2. Wei Zhang
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  3. Liang Ma
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  4. Qichao Zhao
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Corresponding author

Correspondence to Liang Ma .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

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Lu, P., Zhang, W., Ma, L., Zhao, Q. (2020). A Framework of Real-Time Stress Monitoring and Intervention System. In: Rau, PL. (eds) Cross-Cultural Design. Applications in Health, Learning, Communication, and Creativity. HCII 2020. Lecture Notes in Computer Science(), vol 12193. Springer, Cham. https://doi.org/10.1007/978-3-030-49913-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-49913-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49912-9

  • Online ISBN: 978-3-030-49913-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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