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Using Deep Learning Models and Wearable Sensors to Predict Prosthetic Ankle Torques
Inverse dynamics from motion capture is the most common technique for analyzing human biomechanics. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable ofdoi:10.36227/techrxiv.19790236.v1 fatcat:iiwq542ntzhrvd2nmxkge7ccii