• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

使用腕戴式惯性传感器评估伸手动作的质量。

Estimating Quality of Reaching Movement Using a Wrist-Worn Inertial Sensor.

作者信息

Oubre Brandon, Daneault Jean-Francois, Jung Hee-Tae, Park Joonwoo, Ryu Taekyeong, Kim Yangsoo, Lee Sunghoon Ivan

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3719-3722. doi: 10.1109/EMBC44109.2020.9175708.

DOI:10.1109/EMBC44109.2020.9175708
PMID:33018809
Abstract

Stroke is a major cause of long-term disability. Because patients recovering from stroke often perform differently in clinical settings than in their naturalistic environments, remote monitoring of motor performance is needed to evaluate the true impact of prescribed therapies. Wearable sensors have been considered as a technical solution to this problem, but most existing systems focus on measuring the amount of movement without considering the quality of movement. We present a novel method to seamlessly and unobtrusively measure the quality of individual reaching movements by leveraging a motor control theory that describes how the central nervous system plans and executes movements. We trained and evaluated our system on 19 stroke survivors to estimate the Functional Ability Scale (FAS) of reaching movements. The analysis showed that we can estimate the FAS scores of reaching movements, with some confusion between adjacent scores. Furthermore, we estimated the average FAS scores of subjects with a normalized root mean square error (NRMSE) of 22.5%. Though our model's high error on two severe subjects influenced our overall estimation performance, we could accurately estimate scores in most of the mild-to-moderate subjects (NRMSE of 13.1% without the outliers). With further development and testing, we believe the proposed technique can be applied to monitor patient recovery in home and community settings.

摘要

中风是导致长期残疾的主要原因。由于从中风恢复的患者在临床环境中的表现往往与在自然环境中不同,因此需要对运动表现进行远程监测,以评估规定治疗的真正效果。可穿戴传感器被认为是解决这一问题的技术方案,但大多数现有系统只专注于测量运动量,而不考虑运动质量。我们提出了一种新颖的方法,通过利用一种描述中枢神经系统如何规划和执行运动的运动控制理论,无缝且不引人注意地测量个体伸手动作的质量。我们在19名中风幸存者身上训练并评估了我们的系统,以估计伸手动作的功能能力量表(FAS)。分析表明,我们能够估计伸手动作的FAS分数,相邻分数之间存在一些混淆。此外,我们估计受试者的平均FAS分数时,归一化均方根误差(NRMSE)为22.5%。尽管我们的模型在两名严重受试者上的高误差影响了整体估计性能,但我们能够在大多数轻度至中度受试者中准确估计分数(去除异常值后NRMSE为13.1%)。随着进一步的开发和测试,我们相信所提出的技术可应用于家庭和社区环境中监测患者的康复情况。

相似文献

1
Estimating Quality of Reaching Movement Using a Wrist-Worn Inertial Sensor.使用腕戴式惯性传感器评估伸手动作的质量。
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3719-3722. doi: 10.1109/EMBC44109.2020.9175708.
2
Towards the Ambulatory Assessment of Movement Quality in Stroke Survivors using a Wrist-worn Inertial Sensor.使用腕部惯性传感器对中风幸存者运动质量进行动态评估
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2825-2828. doi: 10.1109/EMBC.2018.8512845.
3
Monitoring Arm Movements Post-Stroke for Applications in Rehabilitation and Home Settings.监测脑卒中后手臂运动在康复和家庭环境中的应用。
IEEE Trans Neural Syst Rehabil Eng. 2022;30:2312-2321. doi: 10.1109/TNSRE.2022.3197993. Epub 2022 Aug 22.
4
Detection and Assessment of Point-to-Point Movements During Functional Activities Using Deep Learning and Kinematic Analyses of the Stroke-Affected Wrist.使用深度学习和脑卒中腕部运动分析检测和评估功能活动中的点对点运动。
IEEE J Biomed Health Inform. 2024 Feb;28(2):1022-1030. doi: 10.1109/JBHI.2023.3337156. Epub 2024 Feb 5.
5
Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification.使用k均值聚类分类法通过腕部佩戴的惯性传感器识别上肢运动。
Hum Mov Sci. 2015 Apr;40:59-76. doi: 10.1016/j.humov.2014.11.013. Epub 2014 Dec 19.
6
Portable, open-source solutions for estimating wrist position during reaching in people with stroke.便携式、开源的解决方案,用于估计中风患者在伸手过程中的手腕位置。
Sci Rep. 2021 Nov 18;11(1):22491. doi: 10.1038/s41598-021-01805-2.
7
Wearable Sensor to Monitor Quality of Upper Limb Task Practice for Stroke Survivors at Home.可穿戴传感器在家中监测脑卒中幸存者上肢任务练习的质量。
Sensors (Basel). 2024 Jan 16;24(2):554. doi: 10.3390/s24020554.
8
The Use of a Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings.手指佩戴式加速度计在非卧床环境下监测手部使用情况的应用。
IEEE J Biomed Health Inform. 2019 Mar;23(2):599-606. doi: 10.1109/JBHI.2018.2821136. Epub 2018 Mar 30.
9
Would a thermal sensor improve arm motion classification accuracy of a single wrist-mounted inertial device?腕部单惯性仪的热传感器是否能提高手臂运动分类精度?
Biomed Eng Online. 2019 May 7;18(1):53. doi: 10.1186/s12938-019-0677-7.
10
A novel upper-limb function measure derived from finger-worn sensor data collected in a free-living setting.一种源于在自由生活环境中采集的手指佩戴传感器数据的新型上肢功能测量方法。
PLoS One. 2019 Mar 20;14(3):e0212484. doi: 10.1371/journal.pone.0212484. eCollection 2019.