Translating sEMG Signals to Continuous Hand Poses using Recurrent Neural Networks

In this paper, we propose a hand pose estimation approach from low cost surface electromyogram (sEMG) signals using recurrent neural networks (RNN). We use the Leap Motion sensor to capture the hand joint kinematics and the Myo sensor to collect sEMG while the user is performing simple finger movements. We aim at building an accurate regression model that predicts hand joint kinematics from sEMG features. We use RNN with long short-term memory (LSTM) cells to account for the non-linear relationship between the two domains (sEMG and hand pose). Additionally, we add a Gaussian mixture model (GMM) to build a probabilistic model of hand pose given EMG data. We performed experiments across 7 users to test the performance of our approach. Our results show that for simple hand gestures such as finger flexion, the model is able to capture hand pose kinematics precisely.