Improved Classification Accuracy of Hand Movements Using Softmax Classifier and Kalman Filter

Accurate identification of the intended hand movement from the surface Electromyography (sEMG) data is desired for effective control of myoelectric lower arm prostheses. This study improves the classification accuracy of hand gestures by using feature arrays, Kalman filter (KF), and a Softmax classifier. We use data from BioPatRec database to classify ten hand movements performed by 17 participants. The proposed classifier achieved 95.3% accuracy without KF, and 99.3% accuracy when KF was used to smooth the training data.