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

• Published in: Computational intelligence and neuroscience Vol. 2020; no. 2020; pp. 1 - 12
• Main Authors: Wang, Zhihui, Yang, Jingjing, Ma, Yanli, Guo, Benzhen, Zhang, Xiao
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Arm; Artificial neural networks; Cable television broadcasting industry; Comparative analysis; Control equipment; Correlation analysis; Deep learning; Experiments; Feasibility studies; Feature extraction; Feature selection; Myoelectricity; Neural networks; Pattern recognition; Real time; Rehabilitation; Robot arms; Robot control; Robotics; Robots; Signal processing; Stroke; Support vector machines; Teaching methods; Weight reduction; United States; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2020/8846021

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.