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_{s} =0.896 ) and median frequency (MF) ( r_{s} =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.
CITATION STYLE
Pan, L., Liu, K., & Li, J. (2022). Effect of Subcutaneous Muscle Displacement of Flexor Carpi Radialis on Surface Electromyography. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 1244–1251. https://doi.org/10.1109/TNSRE.2022.3173406
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