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Yang X, Fu Z, Li B, Liu J. An sEMG-Based Human-Exoskeleton Interface Fusing Convolutional Neural Networks With Hand-Crafted Features. Front Neurorobot 2022; 16:938345. [PMID: 35845758 PMCID: PMC9284005 DOI: 10.3389/fnbot.2022.938345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years, the human-robot interfaces (HRIs) based on surface electromyography (sEMG) have been widely used in lower-limb exoskeleton robots for movement prediction during rehabilitation training for patients with hemiplegia. However, accurate and efficient lower-limb movement prediction for patients with hemiplegia remains a challenge due to complex movement information and individual differences. Traditional movement prediction methods usually use hand-crafted features, which are computationally cheap but can only extract some shallow heuristic information. Deep learning-based methods have a stronger feature expression ability, but it is easy to fall into the dilemma of local features, resulting in poor generalization performance of the method. In this article, a human-exoskeleton interface fusing convolutional neural networks with hand-crafted features is proposed. On the basis of our previous study, a lower-limb movement prediction framework (HCSNet) in patients with hemiplegia is constructed by fusing time and frequency domain hand-crafted features and channel synergy learning-based features. An sEMG data acquisition experiment is designed to compare and analyze the effectiveness of HCSNet. Experimental results show that the method can achieve 95.93 and 90.37% prediction accuracy in both within-subject and cross-subject cases, respectively. Compared with related lower-limb movement prediction methods, the proposed method has better prediction performance.
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Affiliation(s)
- Xiao Yang
- Graduate School of Tianjin Medical University, Tianjin, China
| | - Zhe Fu
- Graduate School of Tianjin Medical University, Tianjin, China
| | - Bing Li
- Joint Department, Tianjin Hospital, Tianjin Medical University, Tianjin, China
| | - Jun Liu
- Joint Department, Tianjin Hospital, Tianjin Medical University, Tianjin, China
- *Correspondence: Jun Liu
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