Classification of sEMG Signals for Controlling of a Prosthetic foot using SVM and KNN

Prosthesis plays an important role in rehabilitation. Majority of the powered prosthetic foot available for Trans-tibial amputees (TTA) today take the signal for the control action of the prosthetic foot from the residual stump. Bio-signals from the Biceps Femoris muscle of the thigh is found to be more stable as compared to signals from other thigh muscles and is found to have a reduced metabolic rate during the gait cycle. This study is done on the surface EMG signal measurements of 20 healthy subjects obtained using muscle sensor and conclusions as to which feature extraction technique of the EMG signal is accurate to classify 1-Degree of Freedom (DoF) - dorsiflexion and plantar flexion are derived using weighted KNN and Linear SVM classifier. The comparison of the accuracy of the two classifiers showed that weighted KNN has better efficiency.