Gait Angle Prediction for Lower Limb Orthotics and Prostheses Using an EMG Signal and Neural Networks
نویسنده
چکیده
Commercial lower limb prostheses or orthotics help patients achieve a normal life. However, patients who use such aids need prolonged training to achieve a normal gait, and their fatigability increases. To improve patient comfort, this study proposed a method of predicting gait angle using neural networks and EMG signals. Experimental results using our method show that the absolute average error of the estimated gait angles is 0.25°. This performance data used reference input from a controller for the lower limb orthotic or prosthesis controllers while the patients were walking.
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