Abstract
Electronic knee prosthesis provides a wide range of mobility when compared to mechanical prosthesis. Powered prosthetics are quite expensive and hence are not widely used. In this paper, we have proposed a low-cost above knee powered prosthetic leg which is reliable and the complexity of the model is less. Theprosthesis works by taking inputs from sensor placed in the model and an EMG sensor that records muscle activity of the thigh of the amputee. In order to calculate joint motion and knee angle a variety of methods can be used which include goniometer, inertial measurement units and magnetic encoders. Based on these sensor values, the actuation of the joint is determined. In this paper, we are discussing about an approach that uses sensor data along with muscle activity for actuation. The model classifies the current phase of walking based on the EMG sensor and angle values obtained from the leg. Once the gait phase is determined, the next gait phase is initiated. This enables the prosthesis to be reliable and efficient at the same time being cost effective.
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Baby, C.J., Das, K.J., Venugopal, P. (2020). Design of an Above Knee Low-Cost Powered Prosthetic Leg Using Electromyography and Machine Learning. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_30
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DOI: https://doi.org/10.1007/978-981-15-0184-5_30
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