Muscle-Driven Joint-Torque Estimation Based on Voltage-Torque Mapping of Electrical Impedance Sensing

This article offers an impedance sensing method taking advantage of the conductivity changes due to muscle contraction to estimate muscle-driven joint torques through a convolutional neural network (CNN), where the input images are derived from a finite set of boundary voltage measurements. Guided by a physical model combining the forearm biomechanics and the muscle electric field along with the CNN criteria considering the receptive fields (RFs), the effects of two image formats [for quasi-static (QS) and dynamic (DYN) states] on the CNN performance are experimentally studied on eight human subjects’ forearms using a prototype impedance sensing system. By comparing the CNN-estimated torques with that measured on a haptic device, the findings verify that the impedance-based method can estimate the joint torques driven by both the deep and superficial muscles within 9% errors of the three degrees-of-freedom wrist torque and 10% error of the gripping torque and that it is feasible to share data among a similar group to reduce data collection and time when training a CNN for uses on a new subject.