Abstract
Even though it is well known that physical exercises have numerous emotional and physical health benefits, maintaining a regular exercise routine is quite challenging. Fortunately, there exist technologies that promote physical activity. Nonetheless, almost all of these technologies only target a narrow set of physical exercises (e.g., either running or physical workouts but not both) and are only applicable either in indoor or in outdoor environments, but do not work well in both environments. This paper introduces a real-time segmentation and classification algorithm that recognizes physical exercises and that works well in both indoor and outdoor environments. The proposed algorithm achieves a 95% classification accuracy for five indoor and outdoor exercises, including segmentation error. This accuracy is similar or better than previous works that handled only indoor workouts and those use a vision-based approach. Moreover, while comparable machine learning-based approaches need a lot of training data, the proposed correlation-based method needs only one sample of motion data of each target exercises.
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Acknowledgements
This work was supported by Aoyama Gakuin University, Turku University of Applied Sciences, Business Finland’s Business Ecosystems in Effective Exergaming project.
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Ishii, S., Nkurikiyeyezu, K., Luimula, M., Yokokubo, A., Lopez, G. (2021). ExerSense: Real-Time Physical Exercise Segmentation, Classification, and Counting Algorithm Using an IMU Sensor. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-15-8944-7_15
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DOI: https://doi.org/10.1007/978-981-15-8944-7_15
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