Grip force prediction based on changes in Brachioradialis Muscle Impedance

Abstract

Grip force prediction plays an important role in biomechanical research, sports medicine, and clinical rehabilitation. Most of the current studies in this area only focuses on the characteristic input of surface Electromyography (sEMG) signals, but the acquisition and processing of sEMG are complicated and vulnerable to electromagnetic interference. The impedance signal has the advantages of easy acquisition and processing, strong anti-interference, non-invasive detection, and are widely used in the treatment of neuromuscular diseases. In this paper, impedance technique is introduced into grip force prediction. A single-frequency, low-intensity alternating current (AC) signal is injected into the brachioradialis muscle, and the change in muscle impedance is detected through the electrical effect of the electromagnetic field on biological tissue. Then, the correlation between impedance parameters and grip force changes is discussed, and the Long Short-Term Memory (LSTM) grip force prediction model is established with resistance (R) and phase (P) as feature inputs. The results show that the $r^{2}\_score$ of the grip force prediction model is greater than 0.94 and the mean square error (MSE) is lower than 0.7. This paper restores the actual grip force based on the LSTM prediction model and provides a new implementation idea for grip force prediction.