Nowadays, the process of cognitive or motor rehabilitation is mostly implemented in a traditional form. Paper-pencil cognitive exercises or physical exercises with instruments still dominate over digital environments. However, they require constant supervision by professionals, whose availability is relatively decreasing in an ageing society. Lack of supervision, in turn, results in a loss of motivation to exercise or, at the very least, ineffective, sometimes incorrect, exercise. In addition, traditional rehabilitation mechanisms are often repetitive and tedious. Sometimes a lack of supervision or routine results in a failure to adapt the challenges to the user's current needs.
Digital environments and modern technology have much to offer in this regard. One aspect is gamification mechanisms, which work well in video games and allow players to be engaged for hours in challenges of modulated difficulty. Another aspect is the rapidly developing biosensors and tracking systems that allow the user's activity and biophysiological parameters to be monitored in real time. However, the combination of the benefits of technology and gamification stimulus mechanisms must be done in strict accordance with the user's capabilities in order to make the challenges constructive rather than destructive for the user's body and mental conditions. Ongoing monitoring of effort and mental workload and their synchronization with fatigue in the digital environment, supported by motivational gamification mechanisms, form the foundation of the correct and controlled exercises and rehabilitation.
- American College of Sports Medicine. (2000). Guidelines for exercise testing and prescription. 6th ed. Baltimore, Md: Lippincott Williams & Wilkins.
- Buttussi, F. Chittaro, L., Ranon, R., & Verona, A. (2007). Adaptation of graphics and gameplay in fitness games by exploiting motion and physiological sensors. Smart Graphics, 07, 85-96. DOI: https://doi.org/10.1007/978-3-540-73214-3_8
- Furukado, R., & Hagiwara, G. (2021). Examining the effects of digital gameplay of the racing genre on mood and heart rate. Journal of Digital Life, 1.
- Gómez, L.C., Hervás, R., González, I., & Villarreal, V. (2021). Studying the generalisability of cognitive load measured with eeg. Biomedical Signal Processing and Control, 70, 103032. DOI: https://doi.org/10.1016/j.bspc.2021.103032
- Hagen, K., Chorianopoulos, K., Wang, A. I., Jaccheri, L., & Weie, S. (2016, May). Gameplay as exercise. In Proceedings of the 2016 chi conference extended Abstracts on human factors in computing systems (pp. 1872-1878). DOI: https://doi.org/10.1145/2851581.2892515
- Hart, S.G., & Staveland, L. E. (1988). Development of nasa-tlx (task load index): Results of empirical and theoretical research. In Advances in psychology, 52, 139–183. DOI: https://doi.org/10.1016/S0166-4115(08)62386-9
- Ismail, N. A., Hashim, H. A., & Ahmad Yusof, H. (2022). Physical activity and exergames among older adults: A scoping review. Games for Health Journal, 11(1), 1-17. DOI: https://doi.org/10.1089/g4h.2021.0104
- Kakkos, I., Dimitrakopoulos, G. N., Sun, Y., Yuan, J., Matsopoulos, G.K., Bezerianos, A., & Sun Y. (2021). Eeg ﬁnger prints of task-independent mental workload discrimination. IEEE Journal of Biomedical and Health Informatics, 25(10), 3824–3833. DOI: https://doi.org/10.1109/JBHI.2021.3085131
- Ketelhut, S., Röglin, L., Kircher, E., Martin-Niedecken, A., Ketelhut, R., Hottenrott, K., & Ketelhut, K. (2022). The new way to exercise? Evaluating an innovative heart-rate-controlled exergame. International Journal of Sports Medicine, 43(01), 77-82. DOI: https://doi.org/10.1055/a-1520-4742
- Ladekar, M. Y., Gupta, S. S., Joshi, Y. V., & Manthalkar R. R. (2021). Eeg based visual cognitive workload analysis using multirate iir ﬁlters. Biomedical Signal Processing and Control, 68, 102819. DOI: https://doi.org/10.1016/j.bspc.2021.102819
- Martin-Niedecken, A. L. (2021). Towards balancing fun and exertion in exergames: exploring the impact of movement-based controller devices, exercise concepts, game adaptivity and player modes on player experience and training intensity in different exergame settings, PhD Thesis.
- Masuko, S., & Hoshino, J.A. (2006). Fitness game reflecting heart rate. In Advances in Computer Entertainment Technology, 53. DOI: https://doi.org/10.1145/1178823.1178886
- Niforatos, E., Tran, C., Pappas, I., Giannakos, M. (2021). Goalkeeper: A zero-sum exergame for motivating physical activity. In: Human-Computer Interaction – INTERACT 2021. Lecture Notes in Computer Science, vol 12934. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-85613-7_5
- Parasuraman R. (2011). Neuroergonomics: Brain, cognition, and performance at work. Current directions in psychological science, 20(3), 181-186. DOI: https://doi.org/10.1177/0963721411409176
- Reid, G. B., & Nygren, T. E. (1988). The subjective workload assessment technique: A scaling procedure for measuring mental workload. In Advances in psychology, 52, 185-218. DOI: https://doi.org/10.1016/S0166-4115(08)62387-0
- Stach, T., Graham, T. N., Yim, J., & Rhodes, R. E. (2009). Heart rate control of exercise video games. In Proceedings of Graphics interface 2009 (pp. 125-132).
- Stewart, T.H., Villaneuva, K., Hahn, A., Ortiz-Delatorre, J., Wolf, C., Nguyen, R., Bolter, N.D., Kern, M., & Bagley J.R. (2022). Actual vs. perceived exertion during active virtual reality game exercise. Front Rehabil Sci., 3, 887740. DOI: https://doi.org/10.3389/fresc.2022.887740
- Zammouri, A., Moussa, A. A., & Mebrouk Y. (2018). Brain-computer interface for workload estimation: Assessment of mental efforts in learning processes. Expert Systems with Applications, 112, 138–147. DOI: https://doi.org/10.1016/j.eswa.2018.06.027
- Zhang, P., Wang, X., Chen, J., You, W., & Zhang W. (2019). Spectral and temporal feature learning with two-stream neural networks for mental workload assessment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(6), 1149–1159. DOI: https://doi.org/10.1109/TNSRE.2019.2913400