• 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

基于肌电图手势分类和模型预测控制驱动外骨骼的渐进式康复

Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton.

作者信息

Bonilla Daniel, Bravo Manuela, Bonilla Stephany P, Iragorri Angela M, Mendez Diego, Mondragon Ivan F, Alvarado-Rojas Catalina, Colorado Julian D

机构信息

School of Engineering, Pontificia Universidad Javeriana, Bogota 110231, Colombia.

Neurology, School of Medicine, Hospital Universitario San Ignacio, Bogota 110231, Colombia.

出版信息

Bioengineering (Basel). 2023 Jun 27;10(7):770. doi: 10.3390/bioengineering10070770.

DOI:10.3390/bioengineering10070770
PMID:37508798
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10376571/
Abstract

Stroke is a leading cause of disability and death worldwide, with a prevalence of 200 millions of cases worldwide. Motor disability is presented in 80% of patients. In this context, physical rehabilitation plays a fundamental role for gradually recovery of mobility. In this work, we designed a robotic hand exoskeleton to support rehabilitation of patients after a stroke episode. The system acquires electromyographic (EMG) signals in the forearm, and automatically estimates the movement intention for five gestures. Subsequently, we developed a predictive adaptive control of the exoskeleton to compensate for three different levels of muscle fatigue during the rehabilitation therapy exercises. The proposed system could be used to assist the rehabilitation therapy of the patients by providing a repetitive, intense, and adaptive assistance.

摘要

中风是全球致残和致死的主要原因，全球患病率达2亿例。80%的患者存在运动功能障碍。在此背景下，物理康复对于患者运动能力的逐渐恢复起着至关重要的作用。在这项工作中，我们设计了一种机器人手部外骨骼，以支持中风发作后患者的康复。该系统采集前臂的肌电（EMG）信号，并自动估计五种手势的运动意图。随后，我们开发了外骨骼的预测自适应控制，以补偿康复治疗运动过程中三种不同程度的肌肉疲劳。所提出的系统可通过提供重复性、高强度和自适应的辅助来协助患者的康复治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/aaab86ad0ab6/bioengineering-10-00770-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/8910da5a97cd/bioengineering-10-00770-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/adb75ef50c1f/bioengineering-10-00770-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/7eae59b79187/bioengineering-10-00770-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/bdfe3ac2a790/bioengineering-10-00770-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/b9acf5d2f789/bioengineering-10-00770-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/b2c9d2251226/bioengineering-10-00770-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/625b4b1c68c7/bioengineering-10-00770-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/909564710f0d/bioengineering-10-00770-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/6bba9711defa/bioengineering-10-00770-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/aaab86ad0ab6/bioengineering-10-00770-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/8910da5a97cd/bioengineering-10-00770-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/adb75ef50c1f/bioengineering-10-00770-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/7eae59b79187/bioengineering-10-00770-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/bdfe3ac2a790/bioengineering-10-00770-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/b9acf5d2f789/bioengineering-10-00770-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/b2c9d2251226/bioengineering-10-00770-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/625b4b1c68c7/bioengineering-10-00770-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/909564710f0d/bioengineering-10-00770-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/6bba9711defa/bioengineering-10-00770-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d34c/10376571/aaab86ad0ab6/bioengineering-10-00770-g010.jpg

相似文献

1
Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton.基于肌电图手势分类和模型预测控制驱动外骨骼的渐进式康复
Bioengineering (Basel). 2023 Jun 27;10(7):770. doi: 10.3390/bioengineering10070770.
2
Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection.基于肌肉力量检测的手部关节康复辅助按需外骨骼
Sensors (Basel). 2021 Jun 26;21(13):4372. doi: 10.3390/s21134372.
3
An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation.一种用于双侧康复的肌电图控制的机器人手部外骨骼。
IEEE Trans Haptics. 2015 Apr-Jun;8(2):140-51. doi: 10.1109/TOH.2015.2417570. Epub 2015 Mar 30.
4
Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context.用于在游戏情境中辅助家庭康复的肌电图控制膝关节外骨骼的研发。
Front Neurorobot. 2019 Aug 27;13:67. doi: 10.3389/fnbot.2019.00067. eCollection 2019.
5
Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition.基于运动意图识别的中风下肢外骨骼康复机器人自适应控制综述
Front Neurorobot. 2023 Jul 3;17:1186175. doi: 10.3389/fnbot.2023.1186175. eCollection 2023.
6
Interaction with a Hand Rehabilitation Exoskeleton in EMG-Driven Bilateral Therapy: Influence of Visual Biofeedback on the Users' Performance.肌电驱动双侧治疗中与手部康复外骨骼的交互作用：视觉生物反馈对使用者表现的影响。
Sensors (Basel). 2023 Feb 11;23(4):2048. doi: 10.3390/s23042048.
7
Control of Newly-Designed Wearable Robotic Hand Exoskeleton Based on Surface Electromyographic Signals.基于表面肌电信号的新型可穿戴机器人手部外骨骼控制
Front Neurorobot. 2021 Sep 15;15:711047. doi: 10.3389/fnbot.2021.711047. eCollection 2021.
8
An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation.一种用于慢性中风患者的肌电图驱动的外骨骼手部机器人训练装置：中风康复任务训练系统
IEEE Int Conf Rehabil Robot. 2011;2011:5975340. doi: 10.1109/ICORR.2011.5975340.
9
A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation.用于外骨骼辅助神经康复的手指运动中皮质-肌肉耦合的研究。
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:4610-4. doi: 10.1109/EMBC.2015.7319421.
10
Exoskeleton for post-stroke recovery of ambulation (ExStRA): study protocol for a mixed-methods study investigating the efficacy and acceptance of an exoskeleton-based physical therapy program during stroke inpatient rehabilitation.用于卒中后步行康复的外骨骼（ExStRA）：一项混合方法研究的研究方案，旨在调查基于外骨骼的物理治疗方案在卒中住院康复期间的疗效和接受度。
BMC Neurol. 2020 Jan 28;20(1):35. doi: 10.1186/s12883-020-1617-7.

引用本文的文献

1
Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques.嵌入式系统中的肌电信号：处理与分类技术综述
Biomimetics (Basel). 2025 Mar 10;10(3):166. doi: 10.3390/biomimetics10030166.

本文引用的文献

1
Post-Stroke Rehabilitation: Challenges and New Perspectives.中风后康复：挑战与新视角
J Clin Med. 2023 Jan 10;12(2):550. doi: 10.3390/jcm12020550.
2
Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification.基于子空间辨识的外骨骼手康复数据驱动预测控制
Sensors (Basel). 2022 Oct 9;22(19):7645. doi: 10.3390/s22197645.
3
Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks.基于证据卷积网络的原始肌电信号手指运动识别的可靠性分析。
IEEE Trans Neural Syst Rehabil Eng. 2022;30:96-107. doi: 10.1109/TNSRE.2022.3141593. Epub 2022 Jan 28.
4
Influence of muscle fatigue on motor task performance of the hand and wrist: A systematic review.肌肉疲劳对手和腕部运动任务表现的影响：系统评价。
Hum Mov Sci. 2022 Feb;81:102912. doi: 10.1016/j.humov.2021.102912. Epub 2021 Dec 17.
5
Fatigue-Sensitivity Comparison of sEMG and A-Mode Ultrasound based Hand Gesture Recognition.基于 sEMG 和 A 型超声的手部运动识别的疲劳敏感性比较。
IEEE J Biomed Health Inform. 2022 Apr;26(4):1718-1725. doi: 10.1109/JBHI.2021.3122277. Epub 2022 Apr 14.
6
European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment.欧洲卒中组织和欧洲神经病学会联合发布的卒中后认知障碍指南。
Eur J Neurol. 2021 Dec;28(12):3883-3920. doi: 10.1111/ene.15068. Epub 2021 Sep 13.
7
Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection.基于肌肉力量检测的手部关节康复辅助按需外骨骼
Sensors (Basel). 2021 Jun 26;21(13):4372. doi: 10.3390/s21134372.
8
Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network.利用深度神经网络通过表面肌电图对41种手部和腕部运动进行分类
Front Bioeng Biotechnol. 2021 Jun 9;9:548357. doi: 10.3389/fbioe.2021.548357. eCollection 2021.
9
Clinical Application of Virtual Reality for Upper Limb Motor Rehabilitation in Stroke: Review of Technologies and Clinical Evidence.虚拟现实技术在脑卒中上肢运动康复中的临床应用：技术与临床证据综述
J Clin Med. 2020 Oct 21;9(10):3369. doi: 10.3390/jcm9103369.
10
Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control.Locomo-Net：一种用于基于表面肌电信号的手部运动识别以实现假肢控制的低复杂度深度学习框架。
IEEE J Transl Eng Health Med. 2020 Sep 15;8:2100812. doi: 10.1109/JTEHM.2020.3023898. eCollection 2020.