A CW-CNN regression model-based real-time system for virtual hand control

Abstract

For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand.

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

“…d) Deep Learning: Study [163] achieved low-latency real-time prediction with the Channel-wise-CNN model, where each kernel corresponded to an sEMG channel, and enabled transfer learning by updating only the fully connected layers. LSTMbased [164] found that using all sEMG channels outperformed one-to-one mapping.…”