Regular Papers
International Journal of Control, Automation and Systems 2005; 3(2): 152-158
© The International Journal of Control, Automation, and Systems
Gait Angle Prediction for Lower Limb Orthotics and Prostheses Using an EMG Signal and Neural Networks
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.
Keywords EMG, prosthesis, gait angle predictor, human computer interaction, neural networks, orthotic.
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Regular Papers
International Journal of Control, Automation and Systems 2005; 3(2): 152-158
Published online June 1, 2005
Copyright © The International Journal of Control, Automation, and Systems.
Gait Angle Prediction for Lower Limb Orthotics and Prostheses Using an EMG Signal and Neural Networks
Ju-Won Lee and Gun-Ki Lee*
Gyeongsang National University, Korea
Abstract
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.
Keywords: EMG, prosthesis, gait angle predictor, human computer interaction, neural networks, orthotic.
Vol. 22, No. 9, pp. 2673~2953
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