Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation.
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Abstract
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 (90āĀ±ā5)% for the Lw-CNN and (82.5āĀ±ā3.5)% for the SVM in offline testing of all subjects, which prevails over (84āĀ±ā6)% for the online Lw-CNN and (79āĀ±ā4)% for SVM. The robotic arm control accuracy is (88.5āĀ±ā5.5)%. Significance analysis shows no significant correlation (pā=ā0.056) 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.
Copyright Ā© 2020 Benzhen Guo et al.