Journal
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Volume 2020, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2020/8846021
Keywords
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Funding
- Fundamental Research Funds for Hebei North University [JYT2019004]
- Foundation of the Population Health Informatization in Hebei Province Engineering Technology Research Center [2018005]
- National Undergraduate Training Program for Innovation and Entrepreneurship [201910092003, S201910092015]
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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.
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