Authors:
(1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland;
(2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland;
(7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland;
(8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland;
(9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;
(10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland;
(12) EngD.
We developed a cognitive load detector and two versions of mobile surveys to investigate if gamification can reduce the burden of self-reporting. We have found no impact of the addition of simple game elements such as: progress tracking, avatar, rewards on the amount of time spent in high cognitive load or stress during filling in the surveys. However, this feasibility study yields practical learning related to cognitive load model training, such as: 1) Performance of CNN-based cognitive load detector from PPG signal is boosted via transfer learning on stress detection task. 2) There is a link between the model performance on the source and target task. 3) The minimum length of signal for cognitive load classification is 30 seconds but the addition of extra temporal context can further boost the detection. 4) Matching models to the participants using a small calibration dataset can facilitate finding a detector that can reliably distinguish between high and low cognitive load for each individual.
This work is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement Sano No 857533 and the International Research Agendas programme of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund.
Adamczyk, P., Kuś, M., Marek, S., Pręcikowski, R., Grzeszczyk, M., Malawski, M., Lisowska, A.: Designing personalised gamification of mhealth survey applications pp. 224–231 (2023). https://doi.org/10.5220/0011603800003414
Carlier, S., et al. : Investigating the influence of personalised gamification on mobile survey user experience. Sustainability 13(18), 10434 (2021)
Chow, C.Y., Riantiningtyas, R.R., Kanstrup, M.B., Papavasileiou, M., Liem, G.D., Olsen, A.: Can games change children’s eating behaviour? a review of gamification and serious games. Food Quality and Preference 80, 103823 (2020)
Gan, D.Z., McGillivray, L., Han, J., Christensen, H., Torok, M.: Effect of engagement with digital interventions on mental health outcomes: a systematic review and meta-analysis. Frontiers in digital health 3 (2021)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., … & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220
Grzeszczyk, M. K., Blanco, R., Adamczyk, P., Kus, M., Marek, S., Pręcikowski, R., & Lisowska, A. (2023). CogWear: Can we detect cognitive effort with consumer-grade wearables? (version 1.0.0). PhysioNet
Grzeszczyk, M. K., Lisowska, A., Sitek, A., Lisowska A.: Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?. In 25th International Conference on Mobile Human-Computer Interaction (MobileHCI ’23 Companion), (2023)
Gwizdka, J.: Using stroop task to assess cognitive load. In: Proceedings of the 28th Annual European Conference on Cognitive Ergonomics. pp. 219–222 (2010)
Johnson, D., Deterding, S., Kuhn, K.A., Staneva, A., Stoyanov, S., Hides, L.: Gamification for health and wellbeing: A systematic review of the literature. Internet interventions 6, 89–106 (2016)
Lisowska, A., Wilk, S., Peleg, M.: Catching patient’s attention at the right time to help them undergo behavioural change: Stress classification experiment from blood volume pulse. In: International Conference on Artificial Intelligence in Medicine. pp. 72–82. Springer (2021)
Lisowska, A., Wilk, S., Peleg, M.: Is it a good time to survey you? cognitive load classification from blood volume pulse. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). pp. 137–141. IEEE (2021)
Markova, V., Ganchev, T., Kalinkov, K.: Clas: A database for cognitive load, affect and stress recognition. In: 2019 International Conference on Biomedical Innovations and Applications (BIA). pp. 1–4. IEEE (2019)
Oakley-Girvan, I., et al. : What works best to engage participants in mobile app interventions and e-health: A scoping review. Telemedicine and e-Health (2021)
Rim, B., Sung, N.J., Min, S., Hong, M.: Deep learning in physiological signal data: A survey. Sensors 20(4), 969 (2020)
Saganowski, S., et al.: Consumer wearables and affective computing for wellbeing support. In: MobiQuitous 2020- 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. pp. 482–487 (2020)
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)
Stoet, G.: Psytoolkit: A software package for programming psychological experiments using linux. Behavior research methods 42(4), 1096–1104 (2010)
