Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base

In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, withou...

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Main Authors: Shuang Li, Zhanli Wang, Xiaojing Yin, Zaixiang Pang, Xue Yan
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10375571/
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author Shuang Li
Zhanli Wang
Xiaojing Yin
Zaixiang Pang
Xue Yan
author_facet Shuang Li
Zhanli Wang
Xiaojing Yin
Zaixiang Pang
Xue Yan
author_sort Shuang Li
collection DOAJ
description In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.
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spelling doaj.art-2f636b2a5b8e41c9b9f2d84cc52a739d2024-01-16T00:00:28ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102024-01-013224124810.1109/TNSRE.2023.334663910375571Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule BaseShuang Li0https://orcid.org/0000-0002-5797-5525Zhanli Wang1https://orcid.org/0000-0001-6253-3417Xiaojing Yin2https://orcid.org/0000-0002-5688-9473Zaixiang Pang3https://orcid.org/0000-0002-0739-8520Xue Yan4https://orcid.org/0009-0009-6287-1622School of Mechatronic Engineering, Changchun University of Technology, Changchun, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun, ChinaThird Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, ChinaIn the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.https://ieeexplore.ieee.org/document/10375571/Rehabilitation robotbelief rule baserehabilitation evaluation
spellingShingle Shuang Li
Zhanli Wang
Xiaojing Yin
Zaixiang Pang
Xue Yan
Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Rehabilitation robot
belief rule base
rehabilitation evaluation
title Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base
title_full Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base
title_fullStr Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base
title_full_unstemmed Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base
title_short Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base
title_sort rehabilitation evaluation of upper limb motor function for stroke patients based on belief rule base
topic Rehabilitation robot
belief rule base
rehabilitation evaluation
url https://ieeexplore.ieee.org/document/10375571/
work_keys_str_mv AT shuangli rehabilitationevaluationofupperlimbmotorfunctionforstrokepatientsbasedonbeliefrulebase
AT zhanliwang rehabilitationevaluationofupperlimbmotorfunctionforstrokepatientsbasedonbeliefrulebase
AT xiaojingyin rehabilitationevaluationofupperlimbmotorfunctionforstrokepatientsbasedonbeliefrulebase
AT zaixiangpang rehabilitationevaluationofupperlimbmotorfunctionforstrokepatientsbasedonbeliefrulebase
AT xueyan rehabilitationevaluationofupperlimbmotorfunctionforstrokepatientsbasedonbeliefrulebase