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Practical Suitability of Emotion Recognition from Physiological Signals by Mainstream Smartwatches

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Abstract

In the paper, we analyze the current opportunities and limitations of emotion recognition in real-life situations via mainstream smartwatches (e.g. Apple Watch™). We have identified and taken into account specific real-life situations capable to be recognized by a smartwatch app, where emotion articulation will be superimposed by physiological reactions of the human body. If not handled, such situation would result in misinterpreted emotions. Unfortunately, only one dimension of emotion, tension resp. stress, today can be securely recognized by mainstream smartwatches and only for more strong emotion articulations. To pave the way for the recognition of the other motion dimensions, arousal and valence, we propose a new test scenario, watching soccer games, as an internationally useable, highly scalable and extensively automatable test field. Only with broader experiments in this proposed field the targeted progress in emotion recognition by mainstream smartwatches will be achievable.

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References

  1. Wijasena, H.Z., Ferdiana, R., Wibirama, S.: A survey of emotion recognition using physiological signal in wearable devices. In: AIMS - International Conference on Artificial Intelligence and Mechatronics Systems, IEEE 2021, pp. 1–6 (2021). https://doi.org/10.1109/AIMS52415.2021.9466092

  2. Egger, M., Ley, M., Hanke, S.: Emotion recognition from physiological signal analysis: a review. Electron. Notes Theoret. Comput. Sci. 343, 35–55 (2019). https://doi.org/10.1016/j.entcs.2019.04.009

    Article  Google Scholar 

  3. Saganowski, S., Dutkowiak, A., Dziadek, A., et al.: Emotion recognition using wearables: a systematic literature review-work-in-progress. In: IEEE International Conference on Pervasive Computing and Communications (PERCOM) Workshops. IEEE (2020). https://doi.org/10.1109/PerComWorkshops48775.2020.9156096

  4. Hui, T.K.L., Sherrat, R.S.: Coverage of emotion recognition for common wearable biosensors. Biosensors 8(2), 1–19 (2018). MDPI, https://doi.org/10.3390/bios8020030

  5. Pollreisz, D., TaheriNejad, N.: A simple algorithm for emotion recognition, using physiological signals of a smart watch. In: Annual International Conference IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2353–2356 (2017). https://doi.org/10.1109/EMBC.2017.8037328

  6. Beckmann, N.: Photoplethysmographie-basierte Messung der Pulswellenlaufzeit für die Emotionserkennung, dissertation (in German), University Essen-Duisburg 2019, pp. 1–160

    Google Scholar 

  7. Satamaria-Granados, L., Mendoza-Moreno, J.F., Ramirez-Gonzalez, G.: Tourist recommender systems based on emotion recognition – a scientometric rewiew. Future Internet 13(2), 1–37 (2021). https://doi.org/10.3390/fi13010002

    Article  Google Scholar 

  8. Torrado, J.C., Montoro, G., Gomez, J.: The potential of smartwatches for emotional self-regulation for people with autism spectrum disorder. In: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 5, SCITEPRESS, pp. 444–449 (2016). https://doi.org/10.5220/0005818104440449

  9. Lutze, R., Waldhör, K.: Model based dialogue control for smartwatches. In: Kurosu, M. (ed.) HCI 2017. LNCS, vol. 10272, pp. 225–239. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58077-7_18

    Chapter  Google Scholar 

  10. Lutze, R., Waldhör, K.: Improving dialogue design and control for smartwatches by reinforcement learning based behavioral acceptance patterns. In: Kurosu, M. (ed.) HCII 2020. LNCS, vol. 12183, pp. 75–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49065-2_6

    Chapter  Google Scholar 

  11. Lutze, R., Waldhör, K.: Personal health assistance for elderly people via smartwatch based motion analysis. In: IEEE International Conference on Healthcare Informatics (ICHI), pp. 124–133. IEEE (2017). https://doi.org/10.1109/ICHI.2017.79

  12. Wundt, W.M.: Outlines of Psychology (in German). Engelmann Publishers, Leipzig (1896)

    Google Scholar 

  13. Reisenstein, R.: A structuralist reconstruction of wundt’s three-dimensional theory of emotion. In: Westmeyer, H. (ed.) The Structuralist Program in Psychology: Foundations and Applications, pp. 141–189. Hogrefe & Huber Publishers, Toronto, CN (1992)

    Google Scholar 

  14. Russell, J.A.: A circumplex modell of affect. J. Person. Soc. Psychol. 39(6), 1161–1178 (1980). https://doi.org/10.1037/h0077714

    Article  Google Scholar 

  15. Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3/4), 169–200 (1992). https://doi.org/10.1080/02699939208411068

    Article  Google Scholar 

  16. Liu, Z., Xu, A., Guo, Y., et al.: SEEMO: a computational approach to see emotions. In: Conference on Human Factors in Computing Systems (CHI), Montreal, CN, Paper 464, pp. 1–12, ACM (2018). https://doi.org/10.1145/3173574.3173938

  17. Pal, S., Mukhopadhyay, S., Suryadevara, N.: Development and progress in sensors and technologies for human emotion recognition. MDPI Sens. 21(16), 1–21 (2021). https://doi.org/10.3390/s21165554

    Article  Google Scholar 

  18. Shu, L., Xie, J., Yang, M., et al.: A review of emotion recognition using physiological signals. MDPI Sens. 18(7), 1–41 (12018). https://doi.org/10.3390/s18072074

  19. Dzedzickis, A., Kaklauskas, A., Bucinskas, V.: Human emotion recognition: review of sensors and methods. MDPI Sens. 20(3), 1–40 (2020). https://doi.org/10.3390/s20030592

  20. Zhao, M., Fadel, A., Katabi, D.: Emotion recognition using wireless signals. ACM Commun. 61(9), 91–100 (2018). https://doi.org/10.1145/3236621

    Article  Google Scholar 

  21. Barold, S.S.: Willem Einthoven and the birth of clinical electrocardiography a hundred years ago. Cardiac Electrophysiol. Rev. 7, 99–104 (2003). https://doi.org/10.1023/A:1023667812925

    Article  Google Scholar 

  22. NN: Working with the watchOS App Life Cycle, Developer Information, Apple Inc. https://developer.apple.com/documentation/watchkit/wkextensiondelegate/working_with_the_watchos_app_life_cycle. Accessed 5 Feb 2022

  23. NN: Guide to Background Work, Android Developers, Google Inc. https://developer.android.com/guide/background. Accessed 5 Feb 2022

  24. NN: User Defaults, Developer Information, Apple Inc. https://developer.apple.com/documentation/foundation/userdefaults. Accessed 5 Feb 2022

  25. Lutze, R., Waldhör, K.: Integration of stationary and wearable support services for an actively assisted living of elderly people: capabilities, achievements, limitations, prospects—a case study. In: Wichert, R., Mand, B. (eds.) Ambient Assisted Living. ATSC, pp. 3–26. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52322-4_1

    Chapter  Google Scholar 

  26. Larradet, F., Niewiadomski, R., Barresi, G., et al.: Towards emotion recognition from physiological signals in the wild: approaching the methodological issues in real-life data collection. Front. Psychol. 11(7), 1–23 (2020). Article 1111 https://doi.org/10.3389/fpsyg.2020.01111

  27. Montesinos, V., Dell’Agnola, F., Arza, A. et al.: Multi-modal acute stress recognition using off-the-shelf wearable devices. In: Annual International Conference IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2196–2201 (2019). https://doi.org/10.1109/EMBC.2019.8857130

  28. Heinisch, J.S., Anderson, C., David, K.: Angry of climbing stars? Towards physiological emotion recognition in the wild. In: IEEE International Workshop on Emotion Awareness for Pervasive Computing with Mobile and wearable Devices, p. 486–491 (2019), DOI: https://doi.org/10.1109/PERCOMW.2019.8730725

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Correspondence to Rainer Lutze .

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Lutze, R., Waldhör, K. (2022). Practical Suitability of Emotion Recognition from Physiological Signals by Mainstream Smartwatches. In: Kurosu, M. (eds) Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science, vol 13303. Springer, Cham. https://doi.org/10.1007/978-3-031-05409-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-05409-9_28

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