Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm
A human gait recognition method based on the PSO-ELM algorithm is proposed in order to achieve coordinated movement between humans and lower limb exoskeletons. Ground reaction force (GRF) from the foot, and motion capture data (MCD) from two joints were collected through the exoskeleton device. The...
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MDPI AG
2025-03-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/3/120 |
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| author | Ting Liu Kai Liu Wuyi Luo Jiange Kou Haoran Zhan Guangkai Yu Qing Guo Yan Shi |
| author_facet | Ting Liu Kai Liu Wuyi Luo Jiange Kou Haoran Zhan Guangkai Yu Qing Guo Yan Shi |
| author_sort | Ting Liu |
| collection | DOAJ |
| description | A human gait recognition method based on the PSO-ELM algorithm is proposed in order to achieve coordinated movement between humans and lower limb exoskeletons. Ground reaction force (GRF) from the foot, and motion capture data (MCD) from two joints were collected through the exoskeleton device. The sample data were obtained through multiple experiments in different action scenarios, including standing still, walking on the flat, climbing up and down stairs, traveling up and down slopes, in addition to squatting down and standing up. The algorithm utilizes short-term posture data to recognize different posture movement patterns, with two advantages: (1) A user-friendly wearable device was constructed based on multi-source sensors distributed throughout the body, addressing multiple subjects with varying weights and heights, while being cost-effective and reliably and easily collecting data. (2) The PSO-ELM algorithm identifies key features of gait data, achieving a higher recognition accuracy than other advanced recognition methods, especially during arbitrary gait transition duration. |
| format | Article |
| id | doaj-art-f63a2516f04f450a8cf32636e18d2dff |
| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-f63a2516f04f450a8cf32636e18d2dff2025-08-20T02:11:04ZengMDPI AGActuators2076-08252025-03-0114312010.3390/act14030120Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine AlgorithmTing Liu0Kai Liu1Wuyi Luo2Jiange Kou3Haoran Zhan4Guangkai Yu5Qing Guo6Yan Shi7State Key Laboratory of Power Grid Environmental Protection, China Electric Power Research Institute, Wuhan 430074, ChinaState Key Laboratory of Power Grid Environmental Protection, China Electric Power Research Institute, Wuhan 430074, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100191, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaState Key Laboratory of Power Grid Environmental Protection, China Electric Power Research Institute, Wuhan 430074, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100191, ChinaA human gait recognition method based on the PSO-ELM algorithm is proposed in order to achieve coordinated movement between humans and lower limb exoskeletons. Ground reaction force (GRF) from the foot, and motion capture data (MCD) from two joints were collected through the exoskeleton device. The sample data were obtained through multiple experiments in different action scenarios, including standing still, walking on the flat, climbing up and down stairs, traveling up and down slopes, in addition to squatting down and standing up. The algorithm utilizes short-term posture data to recognize different posture movement patterns, with two advantages: (1) A user-friendly wearable device was constructed based on multi-source sensors distributed throughout the body, addressing multiple subjects with varying weights and heights, while being cost-effective and reliably and easily collecting data. (2) The PSO-ELM algorithm identifies key features of gait data, achieving a higher recognition accuracy than other advanced recognition methods, especially during arbitrary gait transition duration.https://www.mdpi.com/2076-0825/14/3/120cost-effective gait acquisition devicelower limb exoskeletongait recognitionmachine learningPSO-ELM algorithm |
| spellingShingle | Ting Liu Kai Liu Wuyi Luo Jiange Kou Haoran Zhan Guangkai Yu Qing Guo Yan Shi Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm Actuators cost-effective gait acquisition device lower limb exoskeleton gait recognition machine learning PSO-ELM algorithm |
| title | Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm |
| title_full | Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm |
| title_fullStr | Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm |
| title_full_unstemmed | Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm |
| title_short | Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm |
| title_sort | motion gait recognition of lower limb exoskeleton based on particle swarm optimization based extreme learning machine algorithm |
| topic | cost-effective gait acquisition device lower limb exoskeleton gait recognition machine learning PSO-ELM algorithm |
| url | https://www.mdpi.com/2076-0825/14/3/120 |
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