An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model
In the rehabilitation training process for stroke patients, the level of excitement in the patient’s physiological state has a positive impact on the efficacy of the training. In order to improve patients’ initiative during training and prevent dependence on assistive systems, this study proposes an...
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MDPI AG
2024-11-01
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| Series: | Bioengineering |
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| author | Hao Yan Fangcao Zhang Xingao Li Chenchen Zhang Yunjia Zhang Yongfei Feng |
| author_facet | Hao Yan Fangcao Zhang Xingao Li Chenchen Zhang Yunjia Zhang Yongfei Feng |
| author_sort | Hao Yan |
| collection | DOAJ |
| description | In the rehabilitation training process for stroke patients, the level of excitement in the patient’s physiological state has a positive impact on the efficacy of the training. In order to improve patients’ initiative during training and prevent dependence on assistive systems, this study proposes an assist-as-needed control strategy based on a subjective intention decline model. The strategy primarily consists of two modules: a subjective intention decline control module and a limb movement assessment module. The subjective intention decline module collects surface electromyography (sEMG) data during patient training and optimizes support vector machine (SVM) using quantum particle swarm optimization (QPSO) algorithms to establish a subjective intention decline model. The limb movement assessment module collects information such as interaction force and position error during training and proposes a method for evaluating the motion state of the affected limb. This model combines traditional impedance control with a method for assessing limb movement and subjective status, automatically adjusting the level of assistive force on the affected limb in real time to enhance its active participation in tasks. Finally, we performed two verification experiments to assess the patient’s initiative in participating in the training. The experimental results show that the proposed method effectively reduced the average assist force by 65.66% for the traditional impedance control training system and effectively the average assist force by 35.2% for the control training system using only the assist force module based on force position information. At the same time, the accuracy of the subjective intention attenuation module established in the experiment to identify the fatigue level of the subjects reached 93.41%. Therefore, the proposed method effectively improves the initiative of trainers and also prevents patients from relying on the assist-as-needed control training system. |
| format | Article |
| id | doaj-art-3b47917a2bdc4bd684260b53d0824a0c |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-3b47917a2bdc4bd684260b53d0824a0c2024-11-26T17:51:59ZengMDPI AGBioengineering2306-53542024-11-011111111310.3390/bioengineering11111113An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline ModelHao Yan0Fangcao Zhang1Xingao Li2Chenchen Zhang3Yunjia Zhang4Yongfei Feng5School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mathematics and Physics, Hebei University of Engineering, Handan 056038, ChinaSchool of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaSchool of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, ChinaFaculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315000, ChinaIn the rehabilitation training process for stroke patients, the level of excitement in the patient’s physiological state has a positive impact on the efficacy of the training. In order to improve patients’ initiative during training and prevent dependence on assistive systems, this study proposes an assist-as-needed control strategy based on a subjective intention decline model. The strategy primarily consists of two modules: a subjective intention decline control module and a limb movement assessment module. The subjective intention decline module collects surface electromyography (sEMG) data during patient training and optimizes support vector machine (SVM) using quantum particle swarm optimization (QPSO) algorithms to establish a subjective intention decline model. The limb movement assessment module collects information such as interaction force and position error during training and proposes a method for evaluating the motion state of the affected limb. This model combines traditional impedance control with a method for assessing limb movement and subjective status, automatically adjusting the level of assistive force on the affected limb in real time to enhance its active participation in tasks. Finally, we performed two verification experiments to assess the patient’s initiative in participating in the training. The experimental results show that the proposed method effectively reduced the average assist force by 65.66% for the traditional impedance control training system and effectively the average assist force by 35.2% for the control training system using only the assist force module based on force position information. At the same time, the accuracy of the subjective intention attenuation module established in the experiment to identify the fatigue level of the subjects reached 93.41%. Therefore, the proposed method effectively improves the initiative of trainers and also prevents patients from relying on the assist-as-needed control training system.https://www.mdpi.com/2306-5354/11/11/1113subjective intention attenuation modelquantum particle swarm optimization algorithmassist-as-needed control strategy |
| spellingShingle | Hao Yan Fangcao Zhang Xingao Li Chenchen Zhang Yunjia Zhang Yongfei Feng An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model Bioengineering subjective intention attenuation model quantum particle swarm optimization algorithm assist-as-needed control strategy |
| title | An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model |
| title_full | An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model |
| title_fullStr | An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model |
| title_full_unstemmed | An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model |
| title_short | An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model |
| title_sort | assist as needed control strategy based on a subjective intention decline model |
| topic | subjective intention attenuation model quantum particle swarm optimization algorithm assist-as-needed control strategy |
| url | https://www.mdpi.com/2306-5354/11/11/1113 |
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