A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography

Abstract

Handgrip force prediction is widely used in the rehabilitation of the arm and prosthetic control. To investigate the effects of different measurement positions and feature parameters on the results of handgrip force prediction, a model based on electrical impedance myography (EIM) and long short-term memory (LSTM) networks was proposed to compare and determine a better scheme for handgrip force prediction. We conducted the signal acquisition experiments of impedance and handgrip force on the anterior forearm muscles and brachioradialis muscle. Afterwards, three evaluation metrics were introduced to compare the prediction results of various models, and the variability between models was analyzed using paired sample t-tests. The results showed that the model of handgrip force prediction based on anterior forearm muscles exhibited better performance in predicting. The evaluation metrics of $\mathbf {R^{2}}$ , explained variance score (EVS) and normalized mean square error (NMSE) for the model fusing the feature parameters resistance (R) and reactance (X) were 0.9023, 0.9173 and 0.0114, respectively. Therefore, the feature parameters fusing R and X are the optimal input for the handgrip force prediction model. The anterior forearm muscles are the preferred position for impedance measurement over the brachioradialis muscle. This paper validated the feasibility of EIM for handgrip force prediction and provided a new reference and implementation scheme for muscle rehabilitation training and prosthetic control.