Abstract
Regular exercise habit is beneficial for health maintenance and motor enhancement. People realize the importance of regular exercise, but few develop regular exercise habits. Regular exercise habit is hard to cultivate since it affected by various factors, including subjective feeling, exercise expectation, kinetic evaluation, and the same. Such factors could enforce continuous motivations via appreciable positive feedback. However, in practical applications, all the elements are discrete and temporal, which is hard to integrate into a design for motivation enhancement and habit form. Plenty of beginners may affect their future expectations due to a single poor exercise performance and abandon their regular exercise. Continuous positive feedback can help the beginner to establish the causality between single training and future expectations and alleviate anxiety since single poor performance. Wearable technology brings promising prospects for long-term exercise monitoring and provides an interdisciplinary platform for academic attempts. Prior studies have demonstrated wearable efficiency for sports and physical monitoring. Our study presents a motivation enhancement design for individual exercise habits based on multimodal physiological signals to track discrete exercise performance and predict future exercise performance. We applied surface Electromyography (sEMG) to represent the intensity of neuromuscular activation and as the prediction weight. Resting Heart Rate (RHR) to indicate physical fatigue, Sleep Duration (SD) to characterize the rest quality, Sports time (ST), and Heart Rate Difference (HRD) to show the performance of physical improvement. Such factors are integrated into prediction, and the infographic demonstrates forecasts. All participants reported positive psychological feedback to facilitate exercise motivation.
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This work is sponsored by the Shanghai Sailing Program (22YF1430800).
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Liu, X. et al. (2023). Motivation Enhancement Design for Individual Exercise Habits Based on Multimodal Physiological Signals. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14029. Springer, Cham. https://doi.org/10.1007/978-3-031-35748-0_6
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