Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods.

IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Weinan Zhou, Diyang Fu, Zhiyu Duan, Jiping Wang, Linfu Zhou, Liquan Guo
{"title":"Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods.","authors":"Weinan Zhou, Diyang Fu, Zhiyu Duan, Jiping Wang, Linfu Zhou, Liquan Guo","doi":"10.1186/s12984-025-01625-9","DOIUrl":null,"url":null,"abstract":"<p><p>Stroke is a serious cerebrovascular disease, and rehabilitation following the acute phase is particularly crucial. Not all rehabilitation outcomes are favorable, highlighting the necessity for personalized rehabilitation. Precision assessment is essential for tailored rehabilitation interventions. Wearable inertial measurement units (IMUs) and deep learning approaches have been effectively employed for motor function prediction. This study aims to use machine learning techniques and data collected from IMUs to assess the Fugl-Meyer upper extremity subscale for post-stroke patients with motor dysfunction. IMUs signals from 120 patients were collected during a clinical trial. These signals were fed into a gated recurrent unit network to complete the scoring of individual actions, which were then aggregated to obtain the total score. Simultaneously, on the basis of the internal correlation between the Fugl-Meyer assessment and the Brunnstrom scale, Brunnstrom stage prediction models of the arm and hand were established via the random forest and extremely randomized trees algorithm. The experimental results show that the proposed models can score Fugl-Meyer items with a high accuracy of 92.66%. The R<sup>2</sup> between the doctors' score and the model's score is 0.9838. The Brunnstrom stage prediction models can predict high-quality stages, achieving a Spearman correlation coefficient of 0.9709. The application of the proposed method enables precision assessment of patients' upper extremity motor function, thereby facilitating more personalized rehabilitation programs to achieve optimal recovery outcomes. Trial registration: Clinical trial of telerehabilitation training and intelligent evaluation system, ChiCTR2200061310, Registered 20 June 2022-Retrospective registration.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"84"},"PeriodicalIF":5.2000,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001726/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of NeuroEngineering and Rehabilitation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12984-025-01625-9","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Stroke is a serious cerebrovascular disease, and rehabilitation following the acute phase is particularly crucial. Not all rehabilitation outcomes are favorable, highlighting the necessity for personalized rehabilitation. Precision assessment is essential for tailored rehabilitation interventions. Wearable inertial measurement units (IMUs) and deep learning approaches have been effectively employed for motor function prediction. This study aims to use machine learning techniques and data collected from IMUs to assess the Fugl-Meyer upper extremity subscale for post-stroke patients with motor dysfunction. IMUs signals from 120 patients were collected during a clinical trial. These signals were fed into a gated recurrent unit network to complete the scoring of individual actions, which were then aggregated to obtain the total score. Simultaneously, on the basis of the internal correlation between the Fugl-Meyer assessment and the Brunnstrom scale, Brunnstrom stage prediction models of the arm and hand were established via the random forest and extremely randomized trees algorithm. The experimental results show that the proposed models can score Fugl-Meyer items with a high accuracy of 92.66%. The R2 between the doctors' score and the model's score is 0.9838. The Brunnstrom stage prediction models can predict high-quality stages, achieving a Spearman correlation coefficient of 0.9709. The application of the proposed method enables precision assessment of patients' upper extremity motor function, thereby facilitating more personalized rehabilitation programs to achieve optimal recovery outcomes. Trial registration: Clinical trial of telerehabilitation training and intelligent evaluation system, ChiCTR2200061310, Registered 20 June 2022-Retrospective registration.

使用基于imu的可穿戴设备和深度学习方法实现上肢功能临床评分的精确评估。
中风是一种严重的脑血管疾病,急性期后的康复尤为重要。并非所有的康复结果都是有利的,这突出了个性化康复的必要性。精确评估对于量身定制的康复干预至关重要。可穿戴惯性测量单元(imu)和深度学习方法已被有效地用于运动功能预测。本研究旨在利用机器学习技术和从imu收集的数据来评估卒中后运动功能障碍患者的Fugl-Meyer上肢亚量表。在一项临床试验中收集了120名患者的imu信号。这些信号被输入到一个门控循环单元网络中,完成对单个动作的评分,然后将其汇总得到总分。同时,基于Fugl-Meyer评价与Brunnstrom量表之间的内在相关性,通过随机森林和极度随机树算法建立手臂和手部的Brunnstrom阶段预测模型。实验结果表明,该模型能够对Fugl-Meyer项目进行评分,准确率达到92.66%。医生得分与模型得分之间的R2为0.9838。Brunnstrom阶段预测模型可以预测高质量阶段,Spearman相关系数为0.9709。该方法的应用可以精确评估患者的上肢运动功能,从而促进更个性化的康复计划,以达到最佳的康复效果。试验注册:远程康复训练与智能评估系统临床试验,ChiCTR2200061310,注册于2022年6月20日-回顾性注册。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of NeuroEngineering and Rehabilitation
Journal of NeuroEngineering and Rehabilitation 工程技术-工程:生物医学
CiteScore
9.60
自引率
3.90%
发文量
122
审稿时长
24 months
期刊介绍: Journal of NeuroEngineering and Rehabilitation considers manuscripts on all aspects of research that result from cross-fertilization of the fields of neuroscience, biomedical engineering, and physical medicine & rehabilitation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信