Selection of EMG Sensors Based on Motion Coordinated Analysis

Abstract

The intelligent prosthesis driven by electromyography (EMG) signal provides a solution for the movement of the disabled. The proper position of EMG sensors can improve the prosthesis’s motion recognition ability. To exert the amputee’s action-oriented ability and the prosthesis’ control ability, the EMG spatial distribution and internal connection of the prosthetic wearer is analyzed in three kinds of movement conditions: appropriate angle, excessive angle, and angle too small. Firstly, the correlation characteristics between the EMG channels are analyzed by mutual information to construct a muscle functional network. Secondly, the network’s features of different movement conditions are analyzed by calculating the characteristic of nodes and evaluating the importance of nodes. Finally, the convergent cross-mapping method is applied to construct a directed network, and the critical muscle groups which can reflect the user’s movement intention are determined. Experiment shows that this method can accurately determine the EMG location and simplify the distribution of EMG sensors inside the prosthetic socket. The network characteristics of key muscle groups can distinguish different movements effectively and provide a new strategy for decoding the relationship between limb nerve control and body movement.

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

“…For instance, when a person begins to show signs of fatigue between deadlifts, IMUs are expected to show a reduced lift speed. It is hereby possible to track the effects of fatigue on the biomechanics of the body and make appropriate adjustments to the workouts [ 73 ].…”

“…The position of the sEMG sensor significantly affects the quality and reliability of the information collected. When the device is placed further from the muscle that is to be activated, its sensitivity is reduced, and, thus, it will not provide accurate data [ 73 ]. Another example is the accelerometer sensor that tracks the movements of the body when exercising.…”