Joint angle and torque estimation using multi-frequency electrical impedance myography and surface electromyography

This research explores the feasibility of combining surface electromyography (sEMG) and multi-frequency electrical impedance myography (mfEIM) for predicting joint angles and torques. Methods: We utilize a current-limited multi-frequency electrical impedance spectrometer to simultaneously perform mfEIM on the biceps brachii and triceps brachii at 14 frequencies. The same system also measures the sEMG signals using the same electrodes. Measurements are conducted while subjects perform tasks in a one-degree-of-freedom exoskeleton, which enables the measurement of elbow joint torque and angle. We train time-delay neural networks to model the relationships between mfEIM, sEMG, joint torque, and joint angle. Results and Conclusions: The results demonstrate that the sEMG signal can predict joint torque and that mfEIM can predict joint angle. Additionally, we find indications that a combination of sEMG and mfEIM enhances joint torque predictions compared to using sEMG alone. We also determine the delays between the sEMG, mfEIM signals, and the joint angle and torque, finding that the joint torque signal has a delay relative to the sEMG signal of 155ms during an isometric exercise and 125ms during dynamic tasks. Significance: These results suggest that combining sEMG and mfEIM measurements could provide additional insights during biomedical experiments.