Research on Lower Limb Step Speed Recognition Method Based on Electromyography
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Published:2023-02-26
Issue:3
Volume:14
Page:546
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ISSN:2072-666X
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Container-title:Micromachines
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language:en
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Short-container-title:Micromachines
Author:
Zhang Peng1ORCID, Wu Pengcheng2, Wang Wendong3
Affiliation:
1. Engineering Training Centre, Northwestern Polytechnical University, Xi’an 710000, China 2. College of Automation, Northwestern Polytechnical University, Xi’an 710000, China 3. College of Mechanical and Electrical Engineering, Northwestern Polytechnical University, Xi’an 710000, China
Abstract
Wearable exoskeletons play an important role in people’s lives, such as helping stroke and amputation patients to carry out rehabilitation training and so on. How to make the exoskeleton accurately judge the human action intention is the basic requirement to ensure that it can complete the corresponding task. Traditional exoskeleton control signals include pressure values, joint angles and acceleration values, which can only reflect the current motion information of the human lower limbs and cannot be used to predict motion. The electromyography (EMG) signal always occurs before a certain movement; it can be used to predict the target’s gait speed and movement as the input signal. In this study, the generalization ability of a BP neural network and the timing property of a hidden Markov chain are used to properly fuse the two, and are finally used in the research of this paper. Experiments show that, using the same training samples, the recognition accuracy of the three-layer BP neural network is only 91%, while the recognition accuracy of the fusion discriminant model proposed in this paper can reach 95.1%. The results show that the fusion of BP neural network and hidden Markov chain has a strong solving ability for the task of wearable exoskeleton recognition of target step speed.
Funder
Guangdong Basic and Applied Basic Research Foundation Guangdong Science and Technology Innovation Strategy Special Foundation
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
Reference40 articles.
1. Recent advances in rehabilitation robots and intelligent assistance systems;Hou;Acta Autom. Sin.,2016 2. Development of Active Lower Limb Robotic-Based Orthosis and Exoskeleton Devices: A Systematic Review;Kalita;Int. J. Soc. Robot.,2021 3. Pérez Vidal, A.F., Rumbo Morales, J.Y., Ortiz Torres, G., Sorcia Vázquez, F.D.J., Cruz Rojas, A., Brizuela Mendoza, J.A., and Rodríguez Cerda, J.C. (2021). Soft Exoskeletons: Development, Requirements, and Challenges of the Last Decade. Actuators, 10. 4. Islam, M.R., Assad-Uz-Zaman, M., Brahmi, B., Bouteraa, Y., Wang, I., and Rahman, M.H. (2021). Design and Development of an Upper Limb Rehabilitative Robot with Dual Functionality. Micromachines, 12. 5. Wang, X., Feng, Y., Zhang, J., Li, Y., Niu, J., Yang, Y., and Wang, H. (2021). Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients. Machines, 9.
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