GMM-Based Single-Joint Angle Estimation Using EMG Signals

This paper aims to explore the possibility to use Electromyography (EMG) to train a Gaussian Mixture Model (GMM) in order to estimate the bending angle of a single human joint. In particular, EMG signals from eight leg muscles and the knee joint angle are acquired during a kick task from three different subjects. GMM is validated on new unseen data and the classification performances are compared with respect to the number of EMG channels and the number of collected trials used during the training phase. Achieved results show that our framework is able to obtain high performances even using few EMG channels and with a small training dataset (Normalized Mean Square Error: 0.96, 0.98, 0.98 for the three subjects, respectively), opening new and interesting perspectives for the hybrid control of humanoid robots and exoskeletons.