• 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

多通道功能性电刺激对等长环境下人体肘关节运动平衡点的控制

Equilibrium-point control of human elbow-joint movement under isometric environment by using multichannel functional electrical stimulation.

机构信息

Department of Systems Science, Faculty of Engineering Science, Osaka University Osaka, Japan.

Fujitsu Limited Kanagawa, Japan.

出版信息

Front Neurosci. 2014 Jun 17;8:164. doi: 10.3389/fnins.2014.00164. eCollection 2014.

DOI:10.3389/fnins.2014.00164
PMID:24987326
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4060571/
Abstract

Functional electrical stimulation (FES) is considered an effective technique for aiding quadriplegic persons. However, the human musculoskeletal system has highly non-linearity and redundancy. It is thus difficult to stably and accurately control limbs using FES. In this paper, we propose a simple FES method that is consistent with the motion-control mechanism observed in humans. We focus on joint motion by a pair of agonist-antagonist muscles of the musculoskeletal system, and define the "electrical agonist-antagonist muscle ratio (EAA ratio)" and "electrical agonist-antagonist muscle activity (EAA activity)" in light of the agonist-antagonist muscle ratio and agonist-antagonist muscle activity, respectively, to extract the equilibrium point and joint stiffness from electromyography (EMG) signals. These notions, the agonist-antagonist muscle ratio and agonist-antagonist muscle activity, are based on the hypothesis that the equilibrium point and stiffness of the agonist-antagonist motion system are controlled by the central nervous system. We derived the transfer function between the input EAA ratio and force output of the end-point. We performed some experiments in an isometric environment using six subjects. This transfer-function model is expressed as a cascade-coupled dead time element and a second-order system. High-speed, high-precision, smooth control of the hand force were achieved through the agonist-antagonist muscle stimulation pattern determined by this transfer function model.

摘要

功能性电刺激(FES)被认为是辅助四肢瘫痪患者的有效技术。然而,人体肌肉骨骼系统具有高度的非线性和冗余性。因此,很难使用 FES 稳定且准确地控制四肢。在本文中,我们提出了一种与人类观察到的运动控制机制一致的简单 FES 方法。我们专注于肌肉骨骼系统的一对原动肌-拮抗肌的关节运动,并根据原动肌-拮抗肌比和原动肌-拮抗肌活动分别定义“电原动肌-拮抗肌比(EAA 比)”和“电原动肌-拮抗肌活动(EAA 活动)”,从肌电图(EMG)信号中提取平衡点和关节刚度。这些概念,即原动肌-拮抗肌比和原动肌-拮抗肌活动,基于这样一种假设,即原动肌-拮抗肌运动系统的平衡点和刚度由中枢神经系统控制。我们推导出了输入 EAA 比与端点力输出之间的传递函数。我们在等长环境中使用六个受试者进行了一些实验。通过该传递函数模型确定的原动肌-拮抗肌刺激模式,实现了对手力的高速、高精度、平滑控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/876173db5715/fnins-08-00164-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/d638b1ee12b9/fnins-08-00164-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/c229db4628ec/fnins-08-00164-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/0a13879b2be7/fnins-08-00164-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/fcb8b0f66530/fnins-08-00164-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/56ca9f0f2f3e/fnins-08-00164-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/dbccd1ef9492/fnins-08-00164-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/636928a62093/fnins-08-00164-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/928a99515274/fnins-08-00164-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/6b012504c352/fnins-08-00164-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/876173db5715/fnins-08-00164-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/d638b1ee12b9/fnins-08-00164-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/c229db4628ec/fnins-08-00164-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/0a13879b2be7/fnins-08-00164-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/fcb8b0f66530/fnins-08-00164-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/56ca9f0f2f3e/fnins-08-00164-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/dbccd1ef9492/fnins-08-00164-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/636928a62093/fnins-08-00164-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/928a99515274/fnins-08-00164-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/6b012504c352/fnins-08-00164-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e6/4060571/876173db5715/fnins-08-00164-g0010.jpg

相似文献

1
Equilibrium-point control of human elbow-joint movement under isometric environment by using multichannel functional electrical stimulation.多通道功能性电刺激对等长环境下人体肘关节运动平衡点的控制
Front Neurosci. 2014 Jun 17;8:164. doi: 10.3389/fnins.2014.00164. eCollection 2014.
2
Neurorehabilitation with new functional electrical stimulation for hemiparetic upper extremity in stroke patients.采用新型功能性电刺激对脑卒中患者偏瘫上肢进行神经康复治疗。
J Nippon Med Sch. 2008 Feb;75(1):4-14. doi: 10.1272/jnms.75.4.
3
Earable Ω (OMEGA): A Novel Clenching Interface Using Ear Canal Sensing for Human Metacarpophalangeal Joint Control by Functional Electrical Stimulation.可穿戴式 Ω(OMEGA):一种新型的利用耳道感应的紧握界面,通过功能性电刺激实现人类掌指关节控制。
Sensors (Basel). 2022 Sep 29;22(19):7412. doi: 10.3390/s22197412.
4
Impact of the EMG normalization method on muscle activation and the antagonist-agonist co-contraction index during active elbow extension: Practical implications for post-stroke subjects.肌电图归一化方法对主动伸肘时肌肉激活和拮抗-协同收缩指数的影响:对脑卒中后患者的实际意义。
J Electromyogr Kinesiol. 2020 Apr;51:102403. doi: 10.1016/j.jelekin.2020.102403. Epub 2020 Feb 14.
5
Organizing principles for single joint movements: V. Agonist-antagonist interactions.单关节运动的组织原则:V. 主动肌-拮抗肌相互作用
J Neurophysiol. 1992 Jun;67(6):1417-27. doi: 10.1152/jn.1992.67.6.1417.
6
Contributions to the understanding of gait control.对步态控制理解的贡献。
Dan Med J. 2014 Apr;61(4):B4823.
7
Real-Time Closed-Loop Functional Electrical Stimulation Control of Muscle Activation with Evoked Electromyography Feedback for Spinal Cord Injured Patients.基于诱发电位肌电图反馈的实时闭环功能性电刺激肌肉激活控制用于脊髓损伤患者。
Int J Neural Syst. 2018 Aug;28(6):1750063. doi: 10.1142/S0129065717500630. Epub 2017 Dec 25.
8
The timing of control signals underlying fast point-to-point arm movements.快速点对点手臂运动中控制信号的时间安排。
Exp Brain Res. 2001 Apr;137(3-4):411-23. doi: 10.1007/s002210000643.
9
Evaluation of isometric antagonist coactivation strategies of electrically stimulated muscles.
IEEE Trans Biomed Eng. 1996 Feb;43(2):150-60. doi: 10.1109/10.481984.
10
Application of functional electrical stimulation to the paralyzed extremities.功能性电刺激在瘫痪肢体上的应用。
Neurol Med Chir (Tokyo). 1998 Nov;38(11):784-8. doi: 10.2176/nmc.38.784.

引用本文的文献

1
A novel approach to monitoring rehabilitation progress in atrophic muscle using contactless measurement of free oscillations and advanced modal analysis.一种通过非接触式测量自由振荡和先进模态分析来监测萎缩肌肉康复进展的新方法。
Front Bioeng Biotechnol. 2025 Aug 1;13:1496739. doi: 10.3389/fbioe.2025.1496739. eCollection 2025.
2
Physio-avatar EB: aftereffects in error learning with EMG manipulation of first-person avatar experience.生理虚拟人EB:通过肌电图操作第一人称虚拟人体验进行错误学习后的效应
Front Bioeng Biotechnol. 2024 Oct 9;12:1421765. doi: 10.3389/fbioe.2024.1421765. eCollection 2024.
3
Pilot study of the relation between various dynamics of avatar experience and perceptual characteristics.

本文引用的文献

1
FES control of isometric forces in the rat hindlimb using many muscles.使用多块肌肉控制大鼠后肢的等长力量的 FES。
IEEE Trans Biomed Eng. 2013 May;60(5):1422-30. doi: 10.1109/TBME.2013.2237768. Epub 2013 Jan 3.
2
Joint angle control by FES using a feedback error learning controller.使用反馈误差学习控制器通过功能性电刺激进行关节角度控制。
IEEE Trans Neural Syst Rehabil Eng. 2005 Sep;13(3):359-71. doi: 10.1109/TNSRE.2005.847355.
3
Once more on the equilibrium-point hypothesis (lambda model) for motor control.再论运动控制的平衡点假说(拉姆达模型)。
虚拟化身体验的各种动态与感知特征之间关系的初步研究。
PeerJ Comput Sci. 2024 May 21;10:e2042. doi: 10.7717/peerj-cs.2042. eCollection 2024.
4
Earable Ω (OMEGA): A Novel Clenching Interface Using Ear Canal Sensing for Human Metacarpophalangeal Joint Control by Functional Electrical Stimulation.可穿戴式 Ω(OMEGA):一种新型的利用耳道感应的紧握界面,通过功能性电刺激实现人类掌指关节控制。
Sensors (Basel). 2022 Sep 29;22(19):7412. doi: 10.3390/s22197412.
5
Whole-Body Adaptive Functional Electrical Stimulation Kinesitherapy Can Promote the Restoring of Physiological Muscle Synergies for Neurological Patients.全身适应性功能性电刺激运动疗法可促进神经患者生理肌肉协同作用的恢复。
Sensors (Basel). 2022 Feb 13;22(4):1443. doi: 10.3390/s22041443.
6
Muscle Coordination Control for an Asymmetrically Antagonistic-Driven Musculoskeletal Robot Using Attractor Selection.基于吸引子选择的非对称拮抗驱动肌肉骨骼机器人的肌肉协调控制
Appl Bionics Biomech. 2018 Sep 12;2018:9737418. doi: 10.1155/2018/9737418. eCollection 2018.
7
Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics.社论:用于增强感觉运动神经假体的生物信号处理与计算方法
Front Neurosci. 2015 Nov 5;9:434. doi: 10.3389/fnins.2015.00434. eCollection 2015.
J Mot Behav. 1986 Mar;18(1):17-54. doi: 10.1080/00222895.1986.10735369.
4
Reciprocal EMG control of elbow extension by FES.功能性电刺激对肘关节伸展的肌电图交互控制
IEEE Trans Neural Syst Rehabil Eng. 2001 Dec;9(4):338-45. doi: 10.1109/7333.1000113.
5
Model-based control of FES-induced single joint movements.基于模型的功能性电刺激诱导单关节运动控制
IEEE Trans Neural Syst Rehabil Eng. 2001 Sep;9(3):245-57. doi: 10.1109/7333.948452.
6
Sensitivity and versatility of an adaptive system for controlling cyclic movements using functional neuromuscular stimulation.
IEEE Trans Biomed Eng. 2000 Sep;47(9):1287-92. doi: 10.1109/10.867965.
7
Simulated feedforward neural network coordination of hand grasp and wrist angle in a neuroprosthesis.神经假体中手部抓握与腕部角度的模拟前馈神经网络协调
IEEE Trans Rehabil Eng. 2000 Sep;8(3):297-304. doi: 10.1109/86.867871.
8
Neurofuzzy adaptive controlling of selective stimulation for FES: a case study.功能性电刺激选择性刺激的神经模糊自适应控制:一项案例研究。
IEEE Trans Rehabil Eng. 1999 Jun;7(2):183-92. doi: 10.1109/86.769409.
9
Electromyogram-controlled functional electrical stimulation for treatment of the paralyzed upper extremity.
Artif Organs. 1999 May;23(5):466-9. doi: 10.1046/j.1525-1594.1999.06363.x.
10
Three machine learning techniques for automatic determination of rules to control locomotion.
IEEE Trans Biomed Eng. 1999 Mar;46(3):300-10. doi: 10.1109/10.748983.