A neural network model for reconstructing EMG signals from eight shoulder muscles: consequences for rehabilitation robotics and biofeedback.

Abstract

This paper demonstrates the ability of a fully connected feed forward neural network (FFNN) using the backpropagation training algorithm to predict the electromyography (EMG) signal from eight muscles of the shoulder for both exercises not used for training and EMG signals from subjects not used for training. The network showed a good predictive ability for subjects not used for training (r(2) between 0.33 and 0.84) and for activities not used for training (r(2) between 0.56 and 0.89). This may have applications for patients, physical therapists and doctors to monitor patient performance by reviewing the level of agreement between the patient EMG and the predicted EMG. Coupled with traditional methods of evaluation, EMG can provide an excellent measure of how well a patient has responded or is responding to treatment. Incorporating robotic technology could facilitate the use of EMG as an input to an intelligent decision making algorithm used to increase or decrease the level of difficulty according to patient performance.