Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the...

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Detalles Bibliográficos
Autores: Ma, Huiguo, Bao, Yuqi, Lan, Jingfu, Zhu, Xuewen, Wan, Pinwei, Cedazo León, Raquel, Jiang, Shuo, Chen, Fangfang, Kang, Jun, Guo, Qihan, Zhang, Peng
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/419120
Acceso en línea:http://hdl.handle.net/10261/419120
Access Level:acceso abierto
Palabra clave:Bayesian information gain
dynamic axis matching
ankle rehabilitation
hybrid serial-parallel mechanism
kinematic analysis
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spelling Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint RehabilitationMa, HuiguoBao, YuqiLan, JingfuZhu, XuewenWan, PinweiCedazo León, RaquelJiang, ShuoChen, FangfangKang, JunGuo, QihanZhang, PengBayesian information gaindynamic axis matchingankle rehabilitationhybrid serial-parallel mechanismkinematic analysisIn response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical characteristics of the human ankle joint, the design fully draws upon biomimetic principles, constructing a 3-PUU-R hybrid serial–parallel bionic mechanism. By mimicking the dynamic variation of the ankle’s instantaneous motion axis and its balance between stiffness and compliance, a three-dimensional digital model was developed, and multi-posture human factor simulations were conducted, thereby achieving a rehabilitation process more consistent with natural human movement patterns. Natural randomized disability grade experimental data were collected for 100 people to verify the validity of the design results. On this basis, a Bayesian information gain framework was established by quantifying the reduction of uncertainty in rehabilitation outcomes through characteristic parameters, enabling the dynamic optimization of training strategies for personalized and precise ankle rehabilitation. The rehabilitation process was modeled as a problem of uncertainty quantification and information gain optimization. Prior distributions were constructed using surface EMG (electromyography) signals and motion trajectory errors, and mutual information was used to drive the dynamic adjustment of training strategies, ultimately forming a closed-loop control architecture of “demand perception–strategy optimization–execution adaptation.” This innovative integration of probabilistic modeling and cross-joint bionic design overcomes the limitations of single-joint rehabilitation and provides a new paradigm for the development of intelligent rehabilitation devices. The deep integration mechanism-based dynamic axis matching and Bayesian information gain holds significant theoretical value and engineering application prospects for enhancing the effectiveness of neural plasticity training.This research was funded by the National Social Science Fund for Art (21BG125).Peer reviewedMultidisciplinary Digital Publishing InstituteNational Social Science Foundation of ChinaCedazo León, Raquel [0000-0002-4361-4331]Guo, Qihan [0000-0001-9723-883X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/419120reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.3390/biomimetics10120823Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4191202026-05-22T06:33:51Z
dc.title.none.fl_str_mv Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
title Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
spellingShingle Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
Ma, Huiguo
Bayesian information gain
dynamic axis matching
ankle rehabilitation
hybrid serial-parallel mechanism
kinematic analysis
title_short Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
title_full Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
title_fullStr Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
title_full_unstemmed Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
title_sort Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation
dc.creator.none.fl_str_mv Ma, Huiguo
Bao, Yuqi
Lan, Jingfu
Zhu, Xuewen
Wan, Pinwei
Cedazo León, Raquel
Jiang, Shuo
Chen, Fangfang
Kang, Jun
Guo, Qihan
Zhang, Peng
author Ma, Huiguo
author_facet Ma, Huiguo
Bao, Yuqi
Lan, Jingfu
Zhu, Xuewen
Wan, Pinwei
Cedazo León, Raquel
Jiang, Shuo
Chen, Fangfang
Kang, Jun
Guo, Qihan
Zhang, Peng
author_role author
author2 Bao, Yuqi
Lan, Jingfu
Zhu, Xuewen
Wan, Pinwei
Cedazo León, Raquel
Jiang, Shuo
Chen, Fangfang
Kang, Jun
Guo, Qihan
Zhang, Peng
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv National Social Science Foundation of China
Cedazo León, Raquel [0000-0002-4361-4331]
Guo, Qihan [0000-0001-9723-883X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Bayesian information gain
dynamic axis matching
ankle rehabilitation
hybrid serial-parallel mechanism
kinematic analysis
topic Bayesian information gain
dynamic axis matching
ankle rehabilitation
hybrid serial-parallel mechanism
kinematic analysis
description In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical characteristics of the human ankle joint, the design fully draws upon biomimetic principles, constructing a 3-PUU-R hybrid serial–parallel bionic mechanism. By mimicking the dynamic variation of the ankle’s instantaneous motion axis and its balance between stiffness and compliance, a three-dimensional digital model was developed, and multi-posture human factor simulations were conducted, thereby achieving a rehabilitation process more consistent with natural human movement patterns. Natural randomized disability grade experimental data were collected for 100 people to verify the validity of the design results. On this basis, a Bayesian information gain framework was established by quantifying the reduction of uncertainty in rehabilitation outcomes through characteristic parameters, enabling the dynamic optimization of training strategies for personalized and precise ankle rehabilitation. The rehabilitation process was modeled as a problem of uncertainty quantification and information gain optimization. Prior distributions were constructed using surface EMG (electromyography) signals and motion trajectory errors, and mutual information was used to drive the dynamic adjustment of training strategies, ultimately forming a closed-loop control architecture of “demand perception–strategy optimization–execution adaptation.” This innovative integration of probabilistic modeling and cross-joint bionic design overcomes the limitations of single-joint rehabilitation and provides a new paradigm for the development of intelligent rehabilitation devices. The deep integration mechanism-based dynamic axis matching and Bayesian information gain holds significant theoretical value and engineering application prospects for enhancing the effectiveness of neural plasticity training.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/419120
url http://hdl.handle.net/10261/419120
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.3390/biomimetics10120823
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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