Predicting manual wheelchair initiation movement with EMG activity during over ground propulsion.

The Journal of Spinal Cord Medicine Pub Date : 2022-03-01 Epub Date: 2020-07-09 DOI:10.1080/10790268.2020.1778352
Soufien Chikh, Samuel Boudet, Antonio Pinti, Cyril Garnier, Rawad El Hage, Fairouz Azaiez, Eric Watelain
{"title":"Predicting manual wheelchair initiation movement with EMG activity during over ground propulsion.","authors":"Soufien Chikh,&nbsp;Samuel Boudet,&nbsp;Antonio Pinti,&nbsp;Cyril Garnier,&nbsp;Rawad El Hage,&nbsp;Fairouz Azaiez,&nbsp;Eric Watelain","doi":"10.1080/10790268.2020.1778352","DOIUrl":null,"url":null,"abstract":"<p><p><b>Context/Objective:</b> This is a preliminary study of movement finalities prediction in manual wheelchairs (MWCs) from electromyography (EMG) data. MWC users suffer from musculoskeletal disorders and need assistance while moving. The purpose of this work is to predict the direction and speed of movement in MWCs from EMG data prior to movement initiation. This prediction could be used by MWC to assist users in their displacement by doing a smart electrical assistance based on displacement prediction.<b>Design:</b> Experimental study.<b>Setting:</b> Trained Subject LAMIH Laboratory.<b>Participants:</b> Eight healthy subjects trained to move in manual wheelchairs.<b>Interventions:</b> Subjects initiated the movement in three directions (front, right and left) and with two speeds (maximum speed and spontaneous speed) from two hand positions (on the thighs or on the handrim). A total of 96 movements was studied. Activation of 14 muscles was recorded bilaterally at the deltoid anterior, deltoid posterior, biceps brachii, pectoralis major, rectus abdominis, obliquus externus and erector spinae.<b>Outcome Measures:</b> Prior amplitude, prior time and anticipatory postural adjustments were measured. A hierarchical multi-class classification using logistic regression was used to create a cascade of prediction models. We performed a stepwise (forward-backward) selection of variables using the Bayesian information criterion. Percentages of well-classified movements have been measured through the means of a cross-validation.<b>Results:</b> Prediction is possible using the EMG parameters and allows to discriminate the direction / speed combination with 95% correct classification on the 6 possible classes (3 directions * 2 speeds).<b>Conclusion:</b> Action planning in the static position showed significant adaptability to the forthcoming parameters displacement. The percentages of prediction presented in this work make it possible to envision an intuitive assistance to the initiation of the MWC displacement adapted to the user's intentions.</p>","PeriodicalId":501560,"journal":{"name":"The Journal of Spinal Cord Medicine","volume":" ","pages":"262-269"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10790268.2020.1778352","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Spinal Cord Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/10790268.2020.1778352","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/7/9 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Context/Objective: This is a preliminary study of movement finalities prediction in manual wheelchairs (MWCs) from electromyography (EMG) data. MWC users suffer from musculoskeletal disorders and need assistance while moving. The purpose of this work is to predict the direction and speed of movement in MWCs from EMG data prior to movement initiation. This prediction could be used by MWC to assist users in their displacement by doing a smart electrical assistance based on displacement prediction.Design: Experimental study.Setting: Trained Subject LAMIH Laboratory.Participants: Eight healthy subjects trained to move in manual wheelchairs.Interventions: Subjects initiated the movement in three directions (front, right and left) and with two speeds (maximum speed and spontaneous speed) from two hand positions (on the thighs or on the handrim). A total of 96 movements was studied. Activation of 14 muscles was recorded bilaterally at the deltoid anterior, deltoid posterior, biceps brachii, pectoralis major, rectus abdominis, obliquus externus and erector spinae.Outcome Measures: Prior amplitude, prior time and anticipatory postural adjustments were measured. A hierarchical multi-class classification using logistic regression was used to create a cascade of prediction models. We performed a stepwise (forward-backward) selection of variables using the Bayesian information criterion. Percentages of well-classified movements have been measured through the means of a cross-validation.Results: Prediction is possible using the EMG parameters and allows to discriminate the direction / speed combination with 95% correct classification on the 6 possible classes (3 directions * 2 speeds).Conclusion: Action planning in the static position showed significant adaptability to the forthcoming parameters displacement. The percentages of prediction presented in this work make it possible to envision an intuitive assistance to the initiation of the MWC displacement adapted to the user's intentions.

Abstract Image

Abstract Image

在地面推进过程中用肌电活动预测手动轮椅启动运动。
背景/目的:这是一项利用肌电图(EMG)数据预测手动轮椅(mwc)运动终点的初步研究。MWC使用者患有肌肉骨骼疾病,在移动时需要帮助。这项工作的目的是在运动开始之前从肌电图数据预测mwc的运动方向和速度。这一预测可以被MWC利用,通过基于位移预测的智能电气辅助来帮助用户进行位移。设计:实验研究。地点:LAMIH实验室。参与者:8名健康受试者接受了在手动轮椅上移动的训练。干预措施:受试者从两个手部位置(大腿上或手圈上)以三个方向(前、右、左)和两种速度(最大速度和自发速度)开始运动。总共研究了96个动作。记录双侧前三角肌、后三角肌、肱二头肌、胸大肌、腹直肌、外斜肌和竖脊肌14块肌肉的激活情况。结果测量:测量先前幅度、先前时间和预期姿势调整。使用逻辑回归的分层多类分类方法创建了级联的预测模型。我们使用贝叶斯信息标准执行了逐步(向前向后)选择变量。通过交叉验证的方法测量了分类良好的运动的百分比。结果:使用肌电参数进行预测是可能的,并且可以区分方向/速度组合,对6个可能的类别(3个方向* 2个速度)进行95%的正确率分类。结论:静止位置的动作规划对即将到来的参数位移具有显著的适应性。在这项工作中提出的预测百分比使人们有可能设想一种直观的帮助,以启动适应用户意图的MWC位移。
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
自引率
0.00%
发文量
0
×
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