Lower limb motion recognition based on surface electromyography signals and its experimental verification on a novel multi-posture lower limb rehabilitation robots☆. (July 2022)

• Record Type: Journal Article
• Authors: Wang, Bingzhu; Ou, Changwei; Xie, Nenggang; Wang, Lu; Yu, Tiantang; Fan, Guanghui; Chu, Jifa
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: SEMG signals -- Eigenvector -- Pattern recognition -- Lower limb rehabilitation robot -- Multi-posture -- Back propagation (BP) neural network -- Extreme learning machine (ELM); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108067
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Different eigenvectors are tested respectively. Combined with different kinds of BP neural network and ELM classifiers, two motion decoders are constructed for lying and sitting postures, respectively. The experimental platform of this study is a novel multi-posture lower limb rehabilitation robot, which has three postures: lying, reclining and sitting. A simple and effective connection method based on label number retrieval is designed, and two different motion decoders based on lying and sitting are constructed. Through real-time experiments, the effectiveness of active control based on sEMG signals is verified. Abstract: The construction of motion decoder based on surface Electromyography (sEMG) signals is an important part in the clinical trial of rehabilitation robot. Its performance directly determines the success of clinical trial. However, feature extraction is essential in motion decoder. A single feature such as time domain and frequency domain can achieve good classification results, but it is only suitable for a single posture. Hence, for the lying and sitting postures, different feature analysis and their combination are used in this study to improve the sEMG-based lower limb motion classification performance. Nine participants in the clinical trial performed four different movements respectively. Through feature extraction and pattern recognition of sEMG, the trained motion decoders were obtained. The control commands are sent to the robot through labels retrieval to drive the lower limbs for corresponding rehabilitation training. The effectiveness of the based on sEMG signals control method is verified through real-time experimental analysis. Graphical abstract: Image, graphical abstract