Analysis of hand force by EMG measurements

This project investigates the use of myoelectric signals to predict wrist orientation and torque in healthy volunteers and seniors. Surface electromyography (sEMG) signals from forearm muscles were recorded while the volunteers were exerting wrist torque on a custom-made force-sensing platform. Multi-class support vector machines (SVM) were used for classification and regression. The obtained experimental results showed that the SVM method worked well especially in the case of cross-session validation. The proposed sEMG processing scheme enabled classifying wrist torque direction with accuracy higher than 98% for healthy volunteers and 92% for seniors and estimate wrist torque intensity with an average mean square error (MSE) less than 0.08 for regression. The results obtained from the classification and regression showed that the pattern recognition and estimation of sEMG of the forearm muscles is feasible.