IEEE Trans Neural Syst Rehabil Eng. 2022;30:1244-1251. doi: 10.1109/TNSRE.2022.3173406. Epub 2022 May 17.
Changes in joint angle can change the position and orientation of muscle fibers relative to the surface EMG electrode. Our previous study has shown that EMG patterns can identify hand/wrist movements with a greater degree of classification accuracy (CA) when muscle contractions involve a change in the joint angle. The results of this study suggest that changes in the position of the muscle relative to the recording electrode can influence the properties of the recorded EMG signals, however, this was not directly quantified. The present study aims to further investigate the effect of subcutaneous muscle displacement caused by the changes in joint angle on surface EMG signals. Nine able-bodied subjects were tested. The subjects were instructed to perform wrist flexion at five different joint angles (0, 20, 40, 60, and 80) with the same level of muscle contraction. EMG signals and ultrasound images were acquired from the flexor carpi radialis (FCR) simultaneously. Time and frequency domain analysis was adopted to extract features from the EMG signals. The subcutaneous muscle displacement of the FCR relative to the skin surface was measured from the ultrasound images. Spearmans rank correlation coefficient was employed to analyze the correlation between the subcutaneous muscle displacement and the EMG signals. The results showed the subcutaneous muscle displacement of the FCR measured by the ultrasound images was 1 cm when the wrist joint angle changed from 0 to 80. There was a positive relationship between the subcutaneous muscle displacement and the mean absolute value (MAV) ( r = 0.896 ) and median frequency (MF) ( r = 0.849 ) extracted from the EMG signals. The results demonstrated that subcutaneous muscle displacement associated with wrist angle change had a significant effect on FCR EMG signals. This property might have a positive effect on the CA of dynamic tasks.
关节角度的变化会改变肌肉纤维相对于表面肌电电极的位置和方向。我们之前的研究表明,当肌肉收缩涉及关节角度变化时,肌电模式可以更准确地识别手/腕运动。本研究的结果表明,肌肉相对于记录电极的位置变化会影响记录肌电信号的特性,但这并没有直接量化。本研究旨在进一步研究由于关节角度变化导致的肌肉相对于记录电极的位置变化对面部肌电信号的影响。 本研究共纳入了 9 名健康受试者。受试者被要求在相同的肌肉收缩水平下,以五个不同的关节角度(0、20、40、60 和 80)进行腕关节屈曲运动。同时从屈腕肌(FCR)采集肌电信号和超声图像。采用时域和频域分析方法从肌电信号中提取特征。从超声图像中测量 FCR 相对于皮肤表面的皮下肌肉位移。采用 Spearman 秩相关系数分析皮下肌肉位移与肌电信号之间的相关性。结果显示,当腕关节角度从 0 变为 80 时,超声图像测量的 FCR 皮下肌肉位移为 1cm。皮下肌肉位移与肌电信号的均方根绝对值(MAV)(r=0.896)和中值频率(MF)(r=0.849)呈正相关。结果表明,与腕关节角度变化相关的皮下肌肉位移对面部 FCR 肌电信号有显著影响。这种特性可能对动态任务的 CA 有积极影响。