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Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection

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Big Data (BigData 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1320))

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Abstract

As we all know, long-term stress can have a serious impact on human health, which requires continuous and automatic stress monitoring systems. However, there is a lack of commonly used standard data sets for psychological stress detection in affective computing research. Therefore, we present a multimodal dataset for the detection of human stress (MDPSD). A setup was arranged for the synchronized recording of facial videos, photoplethysmography (PPG), and electrodermal activity (EDA) data. 120 participants of different genders and ages were recruited from universities to participate in the experiment. The data collection experiment was divided into eight sessions, including four different kinds of psychological stress stimuli: the classic Stroop Color-Word Test, the Rotation Letter Test, the Stroop Number-Size Test, and the Kraepelin Test. Participants completed the test of each session as required, and then fed back to us the self-assessment stress of each session as our data label. To demonstrate the dataset’s utility, we present an analysis of the correlations between participants’ self-assessments and their physiological responses. Stress is detected using well-known physiological signal features and standard machine learning methods to create a baseline on the dataset. In addition, the accuracy of binary stress recognition achieved 82.60%, and that of three-level stress recognition was 61.04%.

Supported by General Program of National Natural Science Foundation of China (61976078).

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References

  1. Acerbi, G., et al.: A wearable system for stress detection through physiological data analysis. In: Cavallo, F., Marletta, V., Monteriù, A., Siciliano, P. (eds.) ForItAAL 2016. LNEE, vol. 426, pp. 31–50. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54283-6_3

    Chapter  Google Scholar 

  2. Alonso, J., Romero, S., Ballester, M., Antonijoan, R., Mañanas, M.: Stress assessment based on EEG univariate features and functional connectivity measures. Physiol. Measur. 36(7), 1351 (2015)

    Article  Google Scholar 

  3. Anusha, A., Jose, J., Preejith, S., Jayaraj, J., Mohanasankar, S.: Physiological signal based work stress detection using unobtrusive sensors. Biomed. Phys. Eng. Express 4(6), 065001 (2018)

    Google Scholar 

  4. Asif, A., Majid, M., Anwar, S.M.: Human stress classification using EEG signals in response to music tracks. Comput. Biol. Med. 107, 182–196 (2019)

    Article  Google Scholar 

  5. Baltaci, S., Gokcay, D.: Stress detection in human-computer interaction: fusion of pupil dilation and facial temperature features. Int. J. Hum. Comput. Interact. 32(12), 956–966 (2016)

    Article  Google Scholar 

  6. Beatty, J., Lucero-Wagoner, B., et al.: The pupillary system. In: Handbook of Psychophysiology, vol. 2, pp. 142–162 (2000)

    Google Scholar 

  7. Bernardi, L., Wdowczyk-Szulc, J., Valenti, C., Castoldi, S., Passino, C., Spadacini, G., Sleight, P.: Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability. J. Am. Coll. Cardiol. 35(6), 1462–1469 (2000)

    Article  Google Scholar 

  8. Birjandtalab, J., Cogan, D., Pouyan, M.B., Nourani, M.: A non-EEG biosignals dataset for assessment and visualization of neurological status. In: 2016 IEEE International Workshop on Signal Processing Systems (SiPS), pp. 110–114. IEEE (2016)

    Google Scholar 

  9. Cheema, A., Singh, M.: An application of phonocardiography signals for psychological stress detection using non-linear entropy based features in empirical mode decomposition domain. Appl. Soft Comput. 77, 24–33 (2019)

    Article  Google Scholar 

  10. Chrousos, G.P.: Stress and disorders of the stress system. Nat. Rev. Endocrinol. 5(7), 374 (2009)

    Article  Google Scholar 

  11. Fukazawa, Y., Ito, T., Okimura, T., Yamashita, Y., Maeda, T., Ota, J.: Predicting anxiety state using smartphone-based passive sensing. J. Biomed. Inform. 93, 103151 (2019)

    Google Scholar 

  12. Giannakakis, G., et al.: Stress and anxiety detection using facial cues from videos. Biomed. Signal Process. Control 31, 89–101 (2017)

    Article  Google Scholar 

  13. Gjoreski, M., Luštrek, M., Gams, M., Gjoreski, H.: Monitoring stress with a wrist device using context. J. Biomed. Inform. 73, 159–170 (2017)

    Article  Google Scholar 

  14. Handouzi, W., Maaoui, C., Pruski, A., Moussaoui, A.: Short-term anxiety recognition from blood volume pulse signal. In: 2014 IEEE 11th International Multi-Conference on Systems, Signals & Devices (SSD14), pp. 1–6. IEEE (2014)

    Google Scholar 

  15. Healey, J., Picard, R.: SmartCar: detecting driver stress. In: Proceedings 15th International Conference on Pattern Recognition, ICPR-2000, vol. 4, pp. 218–221. IEEE (2000)

    Google Scholar 

  16. Healey, J., Seger, J., Picard, R.: Quantifying driver stress: developing a system for collecting and processing bio-metric signals in natural situations. Biomed. Sci. Instrum. 35, 193–198 (1999)

    Google Scholar 

  17. Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005)

    Article  Google Scholar 

  18. Hou, X., Liu, Y., Sourina, O., Tan, Y.R.E., Wang, L., Mueller-Wittig, W.: EEG based stress monitoring. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3110–3115. IEEE (2015)

    Google Scholar 

  19. Hovsepian, K., al’Absi, M., Ertin, E., Kamarck, T., Nakajima, M., Kumar, S.: cStress: towards a gold standard for continuous stress assessment in the mobile environment. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 493–504. ACM (2015)

    Google Scholar 

  20. Huysmans, D., et al.: Unsupervised learning for mental stress detection-exploration of self-organizing maps. Proc. Biosignals 2018(4), 26–35 (2018)

    Google Scholar 

  21. Khalilzadeh, M.A., Homam, S.M., HOSSEINI, S.A., Niazmand, V.: Qualitative and quantitative evaluation of brain activity in emotional stress (2010)

    Google Scholar 

  22. Kraepelin, E.: Die arbeitscurve, vol. 19. Wilhelm Engelmann (1902)

    Google Scholar 

  23. Kreibig, S.D.: Autonomic nervous system activity in emotion: a review. Biol. Psychol. 84(3), 394–421 (2010)

    Article  Google Scholar 

  24. Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): technical manual and affective ratings. NIMH Center for the Study of Emotion and Attention, vol. 1, pp. 39–58 (1997)

    Google Scholar 

  25. Lee, M.h., Yang, G., Lee, H.K., Bang, S.: Development stress monitoring system based on personal digital assistant (PDA). In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 2364–2367. IEEE (2004)

    Google Scholar 

  26. Lundberg, U., et al.: Psychophysiological stress and EMG activity of the trapezius muscle. Int. J. Behav. Med. 1(4), 354–370 (1994)

    Article  Google Scholar 

  27. McDuff, D., Gontarek, S., Picard, R.: Remote measurement of cognitive stress via heart rate variability. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2957–2960. IEEE (2014)

    Google Scholar 

  28. Minguillon, J., Perez, E., Lopez-Gordo, M.A., Pelayo, F., Sanchez-Carrion, M.J.: Portable system for real-time detection of stress level. Sensors 18(8), 2504 (2018)

    Article  Google Scholar 

  29. Muaremi, A., Bexheti, A., Gravenhorst, F., Arnrich, B., Tröster, G.: Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors. In: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 185–188. IEEE (2014)

    Google Scholar 

  30. Posner, J., Russell, J.A., Peterson, B.S.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(3), 715–734 (2005)

    Article  Google Scholar 

  31. Ramos, J., Hong, J.H., Dey, A.K.: Stress recognition-a step outside the lab. In: PhyCS, pp. 107–118 (2014)

    Google Scholar 

  32. Salahuddin, L., Cho, J., Jeong, M.G., Kim, D.: Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4656–4659. IEEE (2007)

    Google Scholar 

  33. Sano, A., Picard, R.W.: Stress recognition using wearable sensors and mobile phones. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 671–676. IEEE (2013)

    Google Scholar 

  34. de Santos Sierra, A., Ávila, C.S., Casanova, J.G., Del Pozo, G.B.: Real-time stress detection by means of physiological signals. Recent Appl. Biometrics 58, 4857–65 (2011)

    Google Scholar 

  35. Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 2018 on International Conference on Multimodal Interaction, pp. 400–408. ACM (2018)

    Google Scholar 

  36. Sharma, N., Gedeon, T.: Modeling a stress signal. Appl. Soft Comput. 14, 53–61 (2014)

    Article  Google Scholar 

  37. Stroop, J.R.: Studies of interference in serial verbal reactions. J. Exp. Psychol. 18(6), 643 (1935)

    Article  Google Scholar 

  38. Tulen, J., Moleman, P., Van Steenis, H., Boomsma, F.: Characterization of stress reactions to the Stroop color word test. Pharmacol. Biochem. Behav. 32(1), 9–15 (1989)

    Article  Google Scholar 

  39. Visnovcova, Z., et al.: Complexity and time asymmetry of heart rate variability are altered in acute mental stress. Physiol. Meas. 35(7), 1319 (2014)

    Article  Google Scholar 

  40. Wijsman, J., Grundlehner, B., Liu, H., Penders, J., Hermens, H.: Wearable physiological sensors reflect mental stress state in office-like situations. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 600–605. IEEE (2013)

    Google Scholar 

  41. Zhai, J., Barreto, A.: Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1355–1358. IEEE (2006)

    Google Scholar 

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Authors and Affiliations

  1. Hefei University of Technology, Hefei, 230009, China

    Wei Chen, Shixin Zheng & Xiao Sun

Authors
  1. Wei Chen
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  2. Shixin Zheng
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  3. Xiao Sun
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Corresponding author

Correspondence to Xiao Sun .

Editor information

Editors and Affiliations

  1. PLA Academy of Military Sciences, Beijing, China

    Hong Mei

  2. Southwest University, Chongqing, China

    Weiguo Zhang

  3. The University of Edinburgh, Edinburgh, UK

    Wenfei Fan

  4. Southwest University, Chongqing, China

    Zili Zhang

  5. Nanjing University, Nanjing, China

    Yihua Huang

  6. Zhejiang University, Hangzhou, China

    Jiajun Bu

  7. Nanjing University, Nanjing, China

    Yang Gao

  8. Taiyuan University of Technology, Taiyuan, China

    Li Wang

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Chen, W., Zheng, S., Sun, X. (2021). Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection. In: Mei, H., et al. Big Data. BigData 2020. Communications in Computer and Information Science, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-16-0705-9_5

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  • DOI: https://doi.org/10.1007/978-981-16-0705-9_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0704-2

  • Online ISBN: 978-981-16-0705-9

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