Abstract
This paper proposes the AI Supervisor which controls the treadmill speed effectively like an expert such as personal trainer or physical therapist based on real-time sensor data and physical information on the user, and AI decision making. It makes a decision to control the speed of a treadmill during exercise or rehabilitation by measuring the heart rate. The decision is processed by the Deep Neural Network (DNN) with a dataset of 8 people, the accurate decision rate is 94.6%.
This work was supported by the Industrial Strategic Technology Development Program (10076752, Machine learning based personalized lower limb rehabilitation robot system for the patients of stroke and Parkinson’s) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).
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Kim, J., Chang, M., Jeon, D. (2019). The AI Supervisor for the Effective Treadmill Training System of Rehabilitation and Exercise. In: Masia, L., Micera, S., Akay, M., Pons, J. (eds) Converging Clinical and Engineering Research on Neurorehabilitation III. ICNR 2018. Biosystems & Biorobotics, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-01845-0_39
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DOI: https://doi.org/10.1007/978-3-030-01845-0_39
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