The Variation Characteristic of EMG Signal Under Varying Active Torque: A Preliminary Study

Abstract

Surface Electromyography (sEMG) or EMG contains a large amount of information about human kinematics and kinetics, and has been applied in different working environments. Devices like exoskeletons, smart bracelet performs better with information from EMG introduced into the system. For example, some rehabilitation exoskeletons designed for subjects suffered from nerve injuries are controlled under the strategy called “assist-as-needed”. In these studies, various methods, especially machine learning, have been used to establish a large number of nonlinear relationships between EMG and kinematics, as well as kinetics. However, some conditions that have not been studied before but occur in the system will lead to errors in the overall response of the control system. In this paper, human muscle tissue is regarded as a device with input and output responses, the relationship between the least squares slope of AEMG (Averaged EMG) and the current change in muscle contraction torque $\Delta T$ is studied when the torque generated by muscle contraction is $T$, the joint angle is $\theta$, and the joint movement angular velocity is $\omega$. The established relationship provides a potential closed-loop EMG control pathway from human to machine for human-machine interaction devices.