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Conference Paper: Gait Time Parameter Analysis-Based Rehabilitation Evaluation System of Lower Limb Motion Function

TitleGait Time Parameter Analysis-Based Rehabilitation Evaluation System of Lower Limb Motion Function
Authors
KeywordsGait analysis
Gait parameter
Rehabilitation evaluation
Issue Date2022
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, v. 13457 LNAI, p. 90-102 How to Cite?
AbstractAiming at the problems of low accuracy and high time cost of existing motion function rehabilitation evaluation methods, this paper proposes a rehabilitation evaluation method and system of lower limb motion function based on gait parameter analysis. Firstly, according to the calculation method of gait information related parameters, compare the differences of age and gender in healthy people and the differences of gait information between healthy and disabled group, and preliminarily determine the characteristic parameters of lower limb motion function evaluation. Then, the evaluation indexes are determined through multicollinearity and significance analysis, and the multiple linear regression model of Fugl-Meyer assessment (FMA) score is established. The goodness-of-fit test method is used to prove that the proposed evaluation method can replace the lower limb score of FMA as the evaluation method of lower limb motor function in stroke patients. Finally, the lower limb motor function evaluation system is built in the upper computer, so that the model can be used concretely.
Persistent Identifierhttp://hdl.handle.net/10722/327424
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Yue Peng-
dc.contributor.authorCao, Guang Zhong-
dc.contributor.authorChen, Jiang Cheng-
dc.contributor.authorYuan, Ye-
dc.contributor.authorLi, Ling Long-
dc.contributor.authorTan, Dong Po-
dc.contributor.authorLing, Zi Qin-
dc.date.accessioned2023-03-31T05:31:14Z-
dc.date.available2023-03-31T05:31:14Z-
dc.date.issued2022-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, v. 13457 LNAI, p. 90-102-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/327424-
dc.description.abstractAiming at the problems of low accuracy and high time cost of existing motion function rehabilitation evaluation methods, this paper proposes a rehabilitation evaluation method and system of lower limb motion function based on gait parameter analysis. Firstly, according to the calculation method of gait information related parameters, compare the differences of age and gender in healthy people and the differences of gait information between healthy and disabled group, and preliminarily determine the characteristic parameters of lower limb motion function evaluation. Then, the evaluation indexes are determined through multicollinearity and significance analysis, and the multiple linear regression model of Fugl-Meyer assessment (FMA) score is established. The goodness-of-fit test method is used to prove that the proposed evaluation method can replace the lower limb score of FMA as the evaluation method of lower limb motor function in stroke patients. Finally, the lower limb motor function evaluation system is built in the upper computer, so that the model can be used concretely.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectGait analysis-
dc.subjectGait parameter-
dc.subjectRehabilitation evaluation-
dc.titleGait Time Parameter Analysis-Based Rehabilitation Evaluation System of Lower Limb Motion Function-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-031-13835-5_9-
dc.identifier.scopuseid_2-s2.0-85136113474-
dc.identifier.volume13457 LNAI-
dc.identifier.spage90-
dc.identifier.epage102-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000870682100009-

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