Robotic Kinematic measures of the arm in chronic Stroke: part 2 - strong correlation with clinical outcome measures.

Caio B Moretti, Taya Hamilton, Dylan J Edwards, Avrielle Rykman Peltz, Johanna L Chang, Mar Cortes, Alexandre C B Delbe, Bruce T Volpe, Hermano I Krebs
{"title":"Robotic Kinematic measures of the arm in chronic Stroke: part 2 - strong correlation with clinical outcome measures.","authors":"Caio B Moretti,&nbsp;Taya Hamilton,&nbsp;Dylan J Edwards,&nbsp;Avrielle Rykman Peltz,&nbsp;Johanna L Chang,&nbsp;Mar Cortes,&nbsp;Alexandre C B Delbe,&nbsp;Bruce T Volpe,&nbsp;Hermano I Krebs","doi":"10.1186/s42234-021-00082-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models.</p><p><strong>Methods: </strong>Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output.</p><p><strong>Results: </strong>Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model.</p><p><strong>Conclusions: </strong>Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations.</p><p><strong>Trial registration: </strong>http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .</p>","PeriodicalId":72363,"journal":{"name":"Bioelectronic medicine","volume":" ","pages":"21"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715630/pdf/","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioelectronic medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s42234-021-00082-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Background: A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models.

Methods: Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output.

Results: Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model.

Conclusions: Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations.

Trial registration: http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
慢性中风中手臂的机器人运动学测量:第2部分-与临床结果测量的强相关性。
背景:详细的感觉运动评估对于规划有效的、个性化的卒中后治疗至关重要。机器人运动学分析可以提供更好的准确性和分辨率来了解中风恢复情况。在这里,我们研究了手腕远端测量对近端机器人运动学测定的附加值,以提高其与慢性中风患者临床上肢测量的相关性。其次,我们比较了线性和非线性回归模型。方法:数据来源于2012年至2016年进行的一项多中心随机对照试验,研究机器人治疗和经颅直流电刺激(tDCS)的联合效果。24个运动学指标来自4个肩肘任务,35个指标来自3个手腕和前臂评估任务。执行基于相关性的特征选择,仅保持与目标属性基本相关的特征(R > 0.5)非线性模型采用多层感知器神经网络的形式:一个隐藏层和一个线性输出。结果:肩肘指标与Fugl-Meyer评估(上肢,FMA-UE)显著相关,R = 0.82(P结论:腕关节远端运动学与临床结果高度相关,值得未来研究,以探索我们的急性或亚急性中风人群的非线性腕关节模型。试验注册:http://www.clinicaltrials.gov。实际研究开始日期2012年9月。于2012年11月15日首次注册。追溯登记。唯一标识符:NCT01726673和NCT03562663。
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 去求助
来源期刊
CiteScore
6.90
自引率
0.00%
发文量
0
审稿时长
8 weeks
期刊最新文献
In vivo electrophysiology recordings and computational modeling can predict octopus arm movement. Advice for translational neuroscience: move deliberately and build things. Advancing cancer therapy with custom-built alternating electric field devices. Next generation bioelectronic medicine: making the case for non-invasive closed-loop autonomic neuromodulation. Exploring the efficacy of Transcutaneous Auricular Vagus nerve stimulation (taVNS) in modulating local and systemic inflammation in experimental models of colitis.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1