A rehabilitation robot control framework with adaptation of training tasks and robotic assistance
Robot-assisted rehabilitation has exhibited great potential to enhance the motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1244550/full |
| _version_ | 1797667925573763072 |
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| author | Jiajun Xu Kaizhen Huang Tianyi Zhang Kai Cao Aihong Ji Linsen Xu Youfu Li |
| author_facet | Jiajun Xu Kaizhen Huang Tianyi Zhang Kai Cao Aihong Ji Linsen Xu Youfu Li |
| author_sort | Jiajun Xu |
| collection | DOAJ |
| description | Robot-assisted rehabilitation has exhibited great potential to enhance the motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic motion can be regulated through physical human-robot interaction for comfortable support and appropriate assistance level. However, it is hardly possible, especially for patients with severe motor disabilities, to continuously exert force to guide the robot to complete the prescribed training task. Conversely, reduced task difficulty cannot facilitate stimulating patients’ potential movement capabilities. Moreover, challenging more difficult tasks with minimal robotic assistance is usually ignored when subjects show improved performance. In this paper, a control framework is proposed to simultaneously adjust both the training task and robotic assistance according to the subjects’ performance, which can be estimated from the users’ electromyography signals. Concretely, a trajectory deformation algorithm is developed to generate smooth and compliant task motion while responding to pHRI. An assist-as-needed (ANN) controller along with a feedback gain modification algorithm is designed to promote patients’ active participation according to individual performance variance on completing the training task. The proposed control framework is validated using a lower extremity rehabilitation robot through experiments. The experimental results demonstrate that the control scheme can optimize the robotic assistance to complete the subject-adaptation training task with high efficiency. |
| first_indexed | 2024-03-11T20:21:19Z |
| format | Article |
| id | doaj.art-b23c74ec505342d18481b970f0204a00 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-03-11T20:21:19Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-b23c74ec505342d18481b970f0204a002023-10-03T04:33:13ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852023-10-011110.3389/fbioe.2023.12445501244550A rehabilitation robot control framework with adaptation of training tasks and robotic assistanceJiajun Xu0Kaizhen Huang1Tianyi Zhang2Kai Cao3Aihong Ji4Linsen Xu5Youfu Li6College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Mechanical and Electrical Engineering, Hohai University, Changzhou, ChinaDepartment of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, ChinaRobot-assisted rehabilitation has exhibited great potential to enhance the motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic motion can be regulated through physical human-robot interaction for comfortable support and appropriate assistance level. However, it is hardly possible, especially for patients with severe motor disabilities, to continuously exert force to guide the robot to complete the prescribed training task. Conversely, reduced task difficulty cannot facilitate stimulating patients’ potential movement capabilities. Moreover, challenging more difficult tasks with minimal robotic assistance is usually ignored when subjects show improved performance. In this paper, a control framework is proposed to simultaneously adjust both the training task and robotic assistance according to the subjects’ performance, which can be estimated from the users’ electromyography signals. Concretely, a trajectory deformation algorithm is developed to generate smooth and compliant task motion while responding to pHRI. An assist-as-needed (ANN) controller along with a feedback gain modification algorithm is designed to promote patients’ active participation according to individual performance variance on completing the training task. The proposed control framework is validated using a lower extremity rehabilitation robot through experiments. The experimental results demonstrate that the control scheme can optimize the robotic assistance to complete the subject-adaptation training task with high efficiency.https://www.frontiersin.org/articles/10.3389/fbioe.2023.1244550/fullrehabilitation roboticshuman-robot interactionbiological signaltrajectory deformationassist-as-needed control |
| spellingShingle | Jiajun Xu Kaizhen Huang Tianyi Zhang Kai Cao Aihong Ji Linsen Xu Youfu Li A rehabilitation robot control framework with adaptation of training tasks and robotic assistance Frontiers in Bioengineering and Biotechnology rehabilitation robotics human-robot interaction biological signal trajectory deformation assist-as-needed control |
| title | A rehabilitation robot control framework with adaptation of training tasks and robotic assistance |
| title_full | A rehabilitation robot control framework with adaptation of training tasks and robotic assistance |
| title_fullStr | A rehabilitation robot control framework with adaptation of training tasks and robotic assistance |
| title_full_unstemmed | A rehabilitation robot control framework with adaptation of training tasks and robotic assistance |
| title_short | A rehabilitation robot control framework with adaptation of training tasks and robotic assistance |
| title_sort | rehabilitation robot control framework with adaptation of training tasks and robotic assistance |
| topic | rehabilitation robotics human-robot interaction biological signal trajectory deformation assist-as-needed control |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1244550/full |
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