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Electromyographic signals during gait: criteria for envelope filtering and number of strides.

作者信息

Shiavi R, Frigo C, Pedotti A

机构信息

Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee 37235, USA.

出版信息

Med Biol Eng Comput. 1998 Mar;36(2):171-8. doi: 10.1007/BF02510739.

DOI:10.1007/BF02510739
PMID:9684456
Abstract

The use of linear envelopes to represent the electromyographic (EMG) measurements obtained during locomotion has become common practice. Guidelines for designing envelope filters and specifying the minimum number of strides needed to produce valid EMG profiles have been developed. Electromyograms from eight major muscles of the lower leg are measured from five normal young adults during self-selected slow, free and fast walking speeds. 30 strides per task are measured. The 'ideal' EMG profile is defined from the ensemble average of the rectified EMG signal. An error measure is defined and used as a criterion to assess the appropriateness of various cut-off frequencies for envelope filters and the number of strides required for establishing a good EMG profile. It is found that between six and ten strides are needed to form a representative profile, and an envelope filter with a minimum cut-off frequency of approximately 9 Hz is necessary.

摘要

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本文引用的文献

1
Quantification of human dynamic muscle fatigue by electromyography and kinematic profiles.肌电图和运动学特征对人体动态肌肉疲劳的量化评估。
J Electromyogr Kinesiol. 1991;1(1):1-8. doi: 10.1016/1050-6411(91)90021-V.
2
Ensemble averaging of locomotor electromyographic patterns using interpolation.使用插值法对运动肌电图模式进行总体平均。
Med Biol Eng Comput. 1983 Sep;21(5):573-8. doi: 10.1007/BF02442382.
3
Kinematic and EMG patterns during slow, free, and fast walking.慢速、自然行走和快速行走过程中的运动学及肌电图模式。
老年人在坐姿移动任务中使用较少的肌肉来克服干扰。
J Neurophysiol. 2024 Jun 1;131(6):1250-1259. doi: 10.1152/jn.00263.2023. Epub 2024 May 8.
4
Standard isometric contraction has higher reliability than maximum voluntary isometric contraction for normalizing electromyography during level walking among older adults with knee osteoarthritis.对于患有膝关节骨关节炎的老年人,在平地上行走时使肌电图标准化,标准等长收缩比最大自主等长收缩具有更高的可靠性。
Front Bioeng Biotechnol. 2024 Feb 16;12:1276793. doi: 10.3389/fbioe.2024.1276793. eCollection 2024.
5
Joint power, joint work and lower limb muscle activity for transitions between level walking and stair ambulation at three inclinations.在三种倾斜角度下,从水平行走过渡到楼梯行走时的关节功率、关节功和下肢肌肉活动。
PLoS One. 2023 Nov 16;18(11):e0294161. doi: 10.1371/journal.pone.0294161. eCollection 2023.
6
Age-related differences in lower limb muscle activation patterns and balance control strategies while walking over a compliant surface.在顺应性表面上行走时下肢肌肉激活模式和平衡控制策略的年龄相关差异。
Sci Rep. 2023 Oct 2;13(1):16555. doi: 10.1038/s41598-023-43728-0.
7
Intermuscular coherence of plantar and dorsiflexor muscles in older adults with Parkinson's disease and age-matched controls during bipedal and unipedal stance.帕金森病老年患者与年龄匹配的对照组在双足和单足站立期间足底和背屈肌的肌间协调性。
Front Aging Neurosci. 2023 Feb 20;15:1093295. doi: 10.3389/fnagi.2023.1093295. eCollection 2023.
8
Using robot-assisted stiffness perturbations to evoke aftereffects useful to post-stroke gait rehabilitation.利用机器人辅助的刚度扰动来诱发对中风后步态康复有益的后效应。
Front Robot AI. 2023 Jan 4;9:1073746. doi: 10.3389/frobt.2022.1073746. eCollection 2022.
9
Exploring surface electromyography (EMG) as a feedback variable for the human-in-the-loop optimization of lower limb wearable robotics.探索表面肌电图(EMG)作为下肢可穿戴机器人人在回路优化中的反馈变量。
Front Neurorobot. 2022 Oct 6;16:948093. doi: 10.3389/fnbot.2022.948093. eCollection 2022.
10
How to Decide the Number of Gait Cycles in Different Low-Pass Filters to Extract Motor Modules by Non-negative Matrix Factorization During Walking in Chronic Post-stroke Patients.如何确定不同低通滤波器中的步态周期数量,以便在慢性中风后患者行走过程中通过非负矩阵分解提取运动模块。
Front Hum Neurosci. 2022 Apr 6;16:803542. doi: 10.3389/fnhum.2022.803542. eCollection 2022.
J Orthop Res. 1984;2(3):272-80. doi: 10.1002/jor.1100020309.
4
Electromyographic amplitude normalization methods: improving their sensitivity as diagnostic tools in gait analysis.肌电图幅度归一化方法:提高其作为步态分析诊断工具的敏感性。
Arch Phys Med Rehabil. 1984 Sep;65(9):517-21.
5
Dynamic relationship between isometric muscle tension and the electromyogram in man.
J Appl Physiol. 1971 Mar;30(3):345-51. doi: 10.1152/jappl.1971.30.3.345.
6
An application of signal processing techniques to the study of myoelectric signals.信号处理技术在肌电信号研究中的应用。
IEEE Trans Biomed Eng. 1970 Oct;17(4):303-13. doi: 10.1109/tbme.1970.4502758.
7
Repeatability of phasic muscle activity: performance of surface and intramuscular wire electrodes in gait analysis.
J Orthop Res. 1985;3(3):350-9. doi: 10.1002/jor.1100030312.
8
Changes in leg movements and muscle activity with speed of locomotion and mode of progression in humans.人类腿部运动及肌肉活动随运动速度和行进方式的变化。
Acta Physiol Scand. 1985 Apr;123(4):457-75. doi: 10.1111/j.1748-1716.1985.tb07612.x.
9
Predictions of knee and ankle moments of force in walking from EMG and kinematic data.根据肌电图和运动学数据预测步行时膝关节和踝关节的力矩。
J Biomech. 1985;18(1):9-20. doi: 10.1016/0021-9290(85)90041-7.
10
How many strides are required for the analysis of electromyographic data in gait?分析步态中的肌电图数据需要多少步幅?
Scand J Rehabil Med. 1986;18(3):133-5.