Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

Abstract EN

Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection.

Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training.

Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs.

A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition.

The offline and online performance of the two models are then compared.

The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM.

The robotic arm control accuracy is (88.5 ± 5.5)%.

Significance analysis shows no significant correlation (p = 0.056) among real-time control, offline testing, and online testing.

The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.