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Human-Exoskeleton Coupling Dynamics of a Multi-Mode Therapeutic Exoskeleton for Upper Limb Rehabilitation Training
The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early inte...
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Published in: | IEEE access 2021, Vol.9, p.61998-62007 |
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description | The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient's height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients. |
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This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient's height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2021.3072781</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Coupling ; Dynamic models ; Dynamics ; Elbow (anatomy) ; Exoskeletons ; Human-exoskeleton coupling dynamics ; Identification methods ; Mathematical model ; Modelling ; Musculoskeletal system ; Parameter identification ; Prediction models ; Rehabilitation ; rehabilitation training ; Robots ; Root-mean-square errors ; Stroke (medical condition) ; Torque ; Torquemeters ; Training</subject><ispartof>IEEE access, 2021, Vol.9, p.61998-62007</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-7fdd16cf279ef10dbccaff9ac9f4f2badd5a4d60aecceec814be65779d1cc5f83</citedby><cites>FETCH-LOGICAL-c408t-7fdd16cf279ef10dbccaff9ac9f4f2badd5a4d60aecceec814be65779d1cc5f83</cites><orcidid>0000-0002-0240-4004 ; 0000-0001-6886-5498 ; 0000-0003-1171-5987</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9400831$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Xie, Qiaolian</creatorcontrib><creatorcontrib>Meng, Qiaoling</creatorcontrib><creatorcontrib>Zeng, Qingxin</creatorcontrib><creatorcontrib>Fan, Yuanjie</creatorcontrib><creatorcontrib>Dai, Yue</creatorcontrib><creatorcontrib>Yu, Hongliu</creatorcontrib><title>Human-Exoskeleton Coupling Dynamics of a Multi-Mode Therapeutic Exoskeleton for Upper Limb Rehabilitation Training</title><title>IEEE access</title><addtitle>Access</addtitle><description>The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient's height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.</description><subject>Coupling</subject><subject>Dynamic models</subject><subject>Dynamics</subject><subject>Elbow (anatomy)</subject><subject>Exoskeletons</subject><subject>Human-exoskeleton coupling dynamics</subject><subject>Identification methods</subject><subject>Mathematical model</subject><subject>Modelling</subject><subject>Musculoskeletal system</subject><subject>Parameter identification</subject><subject>Prediction models</subject><subject>Rehabilitation</subject><subject>rehabilitation training</subject><subject>Robots</subject><subject>Root-mean-square errors</subject><subject>Stroke (medical condition)</subject><subject>Torque</subject><subject>Torquemeters</subject><subject>Training</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1PwzAMrRBIINgv2CUS546k6VeOqIwPaRMS286RmziQ0TUlbSX492QUIXJxZPu9Z_tF0ZzRBWNU3NxW1XKzWSQ0YQtOi6Qo2Ul0kbBcxDzj-em__3k06_s9Da8Mqay4iPzjeIA2Xn66_h0bHFxLKjd2jW1fyd1XCwereuIMAbIem8HGa6eRbN_QQ4fjYBX5jzTOk13XoScre6jJC75BbRs7wGBDdevBtoH3Kjoz0PQ4-42X0e5-ua0e49Xzw1N1u4pVSsshLozWLFcmKQQaRnWtFBgjQAmTmqQGrTNIdU4BlUJUJUtrzLOiEJoplZmSX0ZPE692sJedtwfwX9KBlT8J518l-LBCg5IWLOcmXMSoOk0UF7VhKaVZmbKgy1ngup64Ou8-RuwHuXejb8P4MsmYoCWnGQ1dfOpS3vW9R_Onyqg8eiUnr-TRK_nrVUDNJ5RFxD-ECPplUP4GA5WSig</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Xie, Qiaolian</creator><creator>Meng, Qiaoling</creator><creator>Zeng, Qingxin</creator><creator>Fan, Yuanjie</creator><creator>Dai, Yue</creator><creator>Yu, Hongliu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient's height and weight and HEC dynamics parameters in the PPT mode. 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subjects | Coupling Dynamic models Dynamics Elbow (anatomy) Exoskeletons Human-exoskeleton coupling dynamics Identification methods Mathematical model Modelling Musculoskeletal system Parameter identification Prediction models Rehabilitation rehabilitation training Robots Root-mean-square errors Stroke (medical condition) Torque Torquemeters Training |
title | Human-Exoskeleton Coupling Dynamics of a Multi-Mode Therapeutic Exoskeleton for Upper Limb Rehabilitation Training |
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