Catching Patient’s Attention at the Right Time to Help Them Undergo Behavioural Change: Stress Classification Experiment from Blood Volume Pulse

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12721)


The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a coaching system recommending personalized evidence-based health behavioral change interventions and supporting patients compliance. Focusing on managing stress via deep breathing intervention, we hypothesise that the patients are more likely to perform suggested breathing exercises when they need calming down. To prompt them at the right time, we developed a machine-learning stress detector based on blood volume pulse that can be measured via consumer-grade smartwatches. We used a publicly available WESAD dataset to evaluate it. Simple 1D CNN achieves 0.837 average F1-score in binary stress vs. non-stress classification and 0.653 in stress vs. amusement vs. neutral classification reaching the state-of-art performance. Personalisation of the population model via fine-tuning on a small number of annotated patient-specific samples yields 12% improvement in stress vs. amusement vs. neutral classification. In future work we will include additional context information to further refine the timing of the prompt and adjust the exercise level.


Blood volume pulse Stress Classification Wearable Fogg behavioral model 



The CAPABLE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875052.


  1. 1.
    Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience, vol. 1990. Harper & Row, New York (1990)Google Scholar
  2. 2.
    Fogg, B.J.: Creating persuasive technologies: an eight-step design process. In: Proceedings of the 4th International Conference on Persuasive Technology, pp. 1–6 (2009)Google Scholar
  3. 3.
    Fogg, B.J.: Tiny Habits: The Small Changes That Change Everything. Houghton Mifflin Harcourt, Boston (2019)Google Scholar
  4. 4.
    Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., Tsiknakis, M.: Review on psychological stress detection using biosignals. IEEE Trans. Affect. Comput. (2019).
  5. 5.
    Greer, J.A., Pirl, W.F., Park, E.R., Lynch, T.J., Temel, J.S.: Behavioral and psychological predictors of chemotherapy adherence in patients with advanced non-small cell lung cancer. J. Psychosom. Res. 65(6), 549–552 (2008)CrossRefGoogle Scholar
  6. 6.
    Gross, M.J., Shearer, D.A., Bringer, J.D., Hall, R., Cook, C.J., Kilduff, L.P.: Abbreviated resonant frequency training to augment heart rate variability and enhance on-demand emotional regulation in elite sport support staff. Appl. Psychophysiol. Biofeedback 41(3), 263–274 (2016). Scholar
  7. 7.
    Hayama, Y., Inoue, T.: The effects of deep breathing on ‘tension-anxiety’ and fatigue in cancer patients undergoing adjuvant chemotherapy. Complement. Ther. Clin. Pract. 18(2), 94–98 (2012)CrossRefGoogle Scholar
  8. 8.
    Indikawati, F.I., Winiarti, S.: Stress detection from multimodal wearable sensor data. In: IOP Conference Series: Materials Science and Engineering, vol. 771, p. 012028. IOP Publishing (2020)Google Scholar
  9. 9.
    Johnson, A.K., Anderson, E.A.: Stress and arousal. Principles of psychophysiology: Physical, social and inferential elements. Cambridge University Press (1990)Google Scholar
  10. 10.
    Lang, P.J.: The emotion probe: studies of motivation and attention. Am. Psychol. 50(5), 372 (1995)CrossRefGoogle Scholar
  11. 11.
    Lisowska, A., O’Neil, A., Poole, I.: Cross-cohort evaluation of machine learning approaches to fall detection from accelerometer data. In: HEALTHINF, pp. 77–82 (2018)Google Scholar
  12. 12.
    McCraty, R., Rees, R.A.: The central role of the heart in generating and sustaining positive emotions. In: Oxford Handbook of Positive Psychology, pp. 527–536 (2009)Google Scholar
  13. 13.
    Page, A.E., Adler, N.E., et al.: Cancer Care for the Whole Patient: Meeting Psychosocial Health Needs. National Academies Press, Washington, D.C. (2008)Google Scholar
  14. 14.
    Peifer, C., Schulz, A., Schächinger, H., Baumann, N., Antoni, C.H.: The relation of flow-experience and physiological arousal under stress–can u shape it? J. Exp. Soc. Psychol. 53, 62–69 (2014)CrossRefGoogle Scholar
  15. 15.
    Pinquart, M., Duberstein, P.: Depression and cancer mortality: a meta-analysis. Psychol. Med. 40(11), 1797–1810 (2010)CrossRefGoogle Scholar
  16. 16.
    Riediger, M., Wrzus, C., Klipker, K., Müller, V., Schmiedek, F., Wagner, G.G.: Outside of the laboratory: associations of working-memory performance with psychological and physiological arousal vary with age. Psychol. Aging 29(1), 103 (2014)CrossRefGoogle Scholar
  17. 17.
    Russell, J.A.: Affective space is bipolar. J. Pers. Soc. Psychol. 37(3), 345–356 (1979)CrossRefGoogle Scholar
  18. 18.
    Saganowski, S., et al.: Review of consumer wearables in emotion, stress, meditation, sleep, and activity detection and analysis. arXiv preprint arXiv:2005.00093 (2020)
  19. 19.
    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 20th ACM International Conference on Multimodal Interaction, pp. 400–408 (2018)Google Scholar
  20. 20.
    Shi, Y., et al.: Personalized stress detection from physiological measurements. In: International Symposium on Quality of Life Technology, pp. 28–29 (2010)Google Scholar
  21. 21.
    Steffen, P.R., Austin, T., DeBarros, A., Brown, T.: The impact of resonance frequency breathing on measures of heart rate variability, blood pressure, and mood. Front. Public Health 5, 222 (2017)CrossRefGoogle Scholar
  22. 22.
    Udovičić, G., Đerek, J., Russo, M., Sikora, M.: Wearable emotion recognition system based on GSR and PPG signals. In: Proceedings of the 2ndInternational Workshop on Multimedia for Personal Health and Health Care, pp.53–59 (2017)Google Scholar
  23. 23.
    Watkins, A.: Coherence: The Secret Science of Brilliant Leadership. Kogan Page Publishers, London (2013)Google Scholar
  24. 24.
    Yu, X., et al.: Activation of the anterior prefrontal cortex and serotonergic system is associated with improvements in mood and EEG changes induced by Zen meditation practice in novices. Int. J. Psychophysiol. 80(2), 103–111 (2011)CrossRefGoogle Scholar
  25. 25.
    Zaccaro, A., et al.: How breath-control can change your life: a systematic review on psycho-physiological correlates of slow breathing. Front. Hum. Neurosci. 12, 353 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland
  2. 2.Department of Information SystemsUniversity of HaifaHaifaIsrael

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