• 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

多部位肌电图幅度估计

Multiple site electromyograph amplitude estimation.

作者信息

Clancy E A, Hogan N

机构信息

Department of Electrical Engineering and Computer Science, Massachusetts of Technology, Cambridge 02139.

出版信息

IEEE Trans Biomed Eng. 1995 Feb;42(2):203-11. doi: 10.1109/10.341833.

DOI:10.1109/10.341833
PMID:7868148
Abstract

Temporal whitening of individual surface electromyograph (EMG) waveforms and spatial combination of multiple recording sites have separately been demonstrated to improve the performance of EMG amplitude estimation. This investigation combined these two techniques by first whitening, then combining the data from multiple EMG recording sites to form an EMG amplitude estimate. A phenomenological mathematical model of multiple sites of the surface EMG waveform, with analytic solution for an optimal amplitude estimate, is presented. Experimental surface EMG waveforms were then sampled from multiple sites during nonfatiguing, constant-force, isometric contractions of the biceps or triceps muscles, over the range of 10-75% maximum voluntary contraction. A signal-to-noise ratio (SNR) was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Results showed that SNR performance: 1) increased with the number of EMG sites, 2) was a function of the sampling frequency, 3) was predominantly invariant to various methods of determining spatial uncorrelation filters, 4) was not sensitive to the intersite correlations of the electrode configuration investigated, and 5) was best at lower levels of contraction. A moving average root mean square estimator (245-ms window) provided an average +/- standard deviation (A +/- SD) SNR of 10.7 +/- 3.3 for single site unwhitened recordings. Temporal whitening and four combined sites improved the A +/- SD SNR to 24.6 +/- 10.4. On one subject, eight whitened combined sites were achieved, providing an A +/- SD SNR or 35.0 +/- 13.4.

摘要

个体表面肌电图(EMG)波形的时间白化以及多个记录部位的空间组合,已分别被证明可提高EMG幅度估计的性能。本研究将这两种技术结合起来,首先对白化,然后将来自多个EMG记录部位的数据进行组合,以形成EMG幅度估计。提出了表面EMG波形多个部位的现象学数学模型,并给出了最优幅度估计的解析解。然后在肱二头肌或肱三头肌进行非疲劳、恒力等长收缩过程中,从多个部位采集实验表面EMG波形,收缩范围为最大自主收缩的10%-75%。从每个幅度估计中计算信噪比(SNR)(估计值围绕平均值的偏差被视为噪声)。结果表明,SNR性能:1)随EMG部位数量增加而提高;2)是采样频率的函数;3)对于确定空间不相关滤波器的各种方法基本不变;4)对所研究电极配置的部位间相关性不敏感;5)在较低收缩水平时最佳。对于单部位未白化记录,移动平均均方根估计器(245毫秒窗口)提供的平均±标准差(A±SD)SNR为10.7±3.3。时间白化和四个组合部位将A±SD SNR提高到24.6±10.4。在一名受试者身上,实现了八个白化组合部位,提供的A±SD SNR为35.0±13.4。

相似文献

1
Multiple site electromyograph amplitude estimation.多部位肌电图幅度估计
IEEE Trans Biomed Eng. 1995 Feb;42(2):203-11. doi: 10.1109/10.341833.
2
Single site electromyograph amplitude estimation.单部位肌电图幅度估计
IEEE Trans Biomed Eng. 1994 Feb;41(2):159-67. doi: 10.1109/10.284927.
3
Influence of smoothing window length on electromyogram amplitude estimates.平滑窗口长度对肌电图幅度估计的影响。
IEEE Trans Biomed Eng. 1998 Jun;45(6):795-800. doi: 10.1109/10.678614.
4
Relating agonist-antagonist electromyograms to joint torque during isometric, quasi-isotonic, nonfatiguing contractions.在等长、准等张、非疲劳性收缩过程中,将激动剂 - 拮抗剂肌电图与关节扭矩相关联。
IEEE Trans Biomed Eng. 1997 Oct;44(10):1024-8. doi: 10.1109/10.634654.
5
Influence of joint angle on the calibration and performance of EMG amplitude estimators.关节角度对肌电图幅度估计器校准和性能的影响。
IEEE Trans Biomed Eng. 1998 May;45(5):664-8. doi: 10.1109/10.668758.
6
A novel approach for estimating muscle fiber conduction velocity by spatial and temporal filtering of surface EMG signals.一种通过对表面肌电信号进行空间和时间滤波来估计肌纤维传导速度的新方法。
IEEE Trans Biomed Eng. 2003 Dec;50(12):1340-51. doi: 10.1109/TBME.2003.819847.
7
Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates.少即是多:高通滤波可去除高达99%的表面肌电信号功率,从而改善基于肌电的肱二头肌肌力估计。
J Electromyogr Kinesiol. 2004 Jun;14(3):389-99. doi: 10.1016/j.jelekin.2003.10.005.
8
Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle.用于从胫骨前肌检测表面肌电图的空间滤波器的选择性。
IEEE Trans Biomed Eng. 2003 Mar;50(3):354-64. doi: 10.1109/TBME.2003.808830.
9
Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis.通过使用高密度肌电图网格和主成分分析来改进基于肌电图的肌肉力量估计。
IEEE Trans Biomed Eng. 2006 Apr;53(4):712-9. doi: 10.1109/TBME.2006.870246.
10
The effects of interelectrode distance on electromyographic amplitude and mean power frequency during isokinetic and isometric muscle actions of the biceps brachii.电极间距对肱二头肌等速和等长肌肉动作期间肌电图幅度和平均功率频率的影响。
J Electromyogr Kinesiol. 2005 Oct;15(5):482-95. doi: 10.1016/j.jelekin.2004.12.001. Epub 2005 Feb 24.

引用本文的文献

1
Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects.四肢健全受试者和肢体缺失受试者对手腕二自由度假肢的肌电控制性能。
IEEE Trans Neural Syst Rehabil Eng. 2022;30:893-904. doi: 10.1109/TNSRE.2022.3163149. Epub 2022 Apr 11.
2
Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets.简化的时变肌电标准差(EMGσ)最优估计:在两个数据集上的评估。
Sensors (Basel). 2021 Jul 30;21(15):5165. doi: 10.3390/s21155165.
3
The Effect of ICA and Non-negative Matrix Factorization Analysis for EMG Signals Recorded From Multi-Channel EMG Sensors.
独立成分分析和非负矩阵分解分析对多通道肌电传感器记录的肌电信号的影响。
Front Neurosci. 2020 Dec 1;14:600804. doi: 10.3389/fnins.2020.600804. eCollection 2020.
4
Automated Channel Selection in High-Density sEMG for Improved Force Estimation.高密度 sEMG 中的自动通道选择,以提高力估计。
Sensors (Basel). 2020 Aug 27;20(17):0. doi: 10.3390/s20174858.
5
Two degrees of freedom, dynamic, hand-wrist EMG-force using a minimum number of electrodes.两个自由度,动态,使用最少数量电极的手腕肌电图-力
J Electromyogr Kinesiol. 2019 Aug;47:10-18. doi: 10.1016/j.jelekin.2019.04.003. Epub 2019 Apr 16.
6
A Comparative Approach to Hand Force Estimation using Artificial Neural Networks.一种使用人工神经网络进行手部力量估计的比较方法。
Biomed Eng Comput Biol. 2012 Jul 30;4:1-15. doi: 10.4137/BECB.S9335. eCollection 2012.
7
The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury.功能性电刺激诱发肌电图电位评估脊髓损伤个体肌肉力量和疲劳的有效性。
Sensors (Basel). 2014 Jul 14;14(7):12598-622. doi: 10.3390/s140712598.
8
Influence of advanced electromyogram (EMG) amplitude processors on EMG-to-torque estimation during constant-posture, force-varying contractions.先进肌电图(EMG)幅度处理器对恒定姿势、力变化收缩过程中肌电图到扭矩估计的影响。
J Biomech. 2006;39(14):2690-8. doi: 10.1016/j.jbiomech.2005.08.007. Epub 2005 Oct 20.
9
Customized interactive robotic treatment for stroke: EMG-triggered therapy.针对中风的定制交互式机器人治疗:肌电图触发疗法。
IEEE Trans Neural Syst Rehabil Eng. 2005 Sep;13(3):325-34. doi: 10.1109/TNSRE.2005.850423.
10
Surface electromyography and mechanomyography recording: a new differential composite probe.
Med Biol Eng Comput. 2003 Nov;41(6):665-9. doi: 10.1007/BF02349974.