手腕运动下的动态肌电解码研究
作者:
作者单位:

东南大学仪器科学与工程学院

基金项目:

国家重点研发计划;江苏省前沿引领技术基础研究专项;江苏省自然科学基金


Research on Dynamic Electromyography Decoding under Wrist Movements
Author:
Affiliation:

School of Instrument Science and Engineering, Southeast University

Fund Project:

National Key Research and Development Program of China;the Basic Research Project of Leading Technology of Jiangsu Province;the Natural Science Foundation of Jiangsu Province

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    摘要:

    手腕是人体最灵活的部位之一,通过对表面肌电信号(Surface Electromyography, sEMG)进行分解能够有效地解码出人体运动的深层次神经驱动。目前关于手腕力矩的研究基本都集中在等长收缩下,动态下的神经驱动解码依然需要大量研究。本文研究了手腕在不同阻力下运动时的运动单元(Motor Unit, MU)分解,具体来说,本文通过磁流变阻尼器来设置不同的阻力水平,将采集到的完整肌电信号划分成运动单元动作电位(Motor Unit Action Potential, MUAP)变化微小的短区间,再在每个短区间上使用静态分解算法来获得运动单元尖峰序列(Motor Unit Spike Train, MUST),并通过短区间的重叠部分来对MU进行追踪,从而得到完整的发放序列。本文研究了在20% 最大自愿收缩力(Maximum Voluntary Contraction, MVC)、40% MVC、60% MVC三种阻力下,腕部伸展和屈曲时的运动单元分解。结果表明,三种阻力下,本文的动态分解算法能够有效地从小臂肌电信号中分解出MU,随着阻力的增加,分解出的MU数目有所下降。手腕伸展过程最多能分解出10±1个MU,脉冲信噪比(Pulse-to-noise Ratio, PNR)和轮廓系数(Silhouette Coefficient, SIL)分别能够达到19.87±1.42dB和0.91±0.03,屈曲过程最多能分解出22±3个MU,PNR和SIL值分别能够达到20.69±2.14dB和0.92±0.03。本研究表明不同阻力下对手腕运动的肌电信号进行神经解码是可行的,对高密度肌电的动态应用有着重要意义。

    Abstract:

    The wrist is one of the most flexible parts of the human body. By decomposing the surface electromyography (sEMG) signals, the deep neural drive of human body movements can be effectively reversed. Currently, research on wrist moments is basically focused on isometric contractions, and substantial research is still needed for the decoding of neural drives under dynamic conditions. This paper studied the decomposition of motor units (MUs) when the wrist moves under different resistances. Specifically, different resistance levels were set by using a magnetorheological damper. The collected complete electromyography signals were divided into short intervals where the changes in motor unit action potentials (MUAPs) are minimal. Then, a classic decomposition algorithm was used in each short interval to obtain the motor unit spike train (MUST), and the MUs were tracked through the overlapping parts of the short intervals, so as to obtain the complete firing sequence. This paper studied the decomposition of motor units when the wrist extended and flexed under three resistances of 20% maximum voluntary contraction (MVC), 40% MVC, and 60% MVC. The results showed that under the three types of resistances, the dynamic decomposition algorithm proposed in this paper could effectively decompose motor units (MUs) from the forearm electromyographic signals, and as the resistance increased, the decomposition effect decreased to some extent. During wrist extension, up to 10 ± 1 MUs could be decomposed, and the pulse-to-noise ratio (PNR) and silhouette distance (SIL) could reach 19.87 ± 1.42 dB and 0.91 ± 0.03 respectively. During wrist flexion, up to 22 ± 3 MUs could be decomposed, and the PNR and SIL values could reach 20.69 ± 2.14 dB and 0.92 ± 0.03 respectively. This study indicates that it is feasible to perform neural decoding from the EMG of wrist movements under different resistances, which has important implications for the application of high-density surface EMG (HD-sEMG) under dynamic contractions.

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杨心昊,徐宝国,宋爱国.手腕运动下的动态肌电解码研究[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2025-01-14
  • 最后修改日期:2025-02-17
  • 录用日期:2025-02-21

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