Preconditioning electromyographic data for an upper extremity model using neural networks

A backpropagation neural network has been employed to precondition the electromyographic signal (EMG) that drives a computational model of the human elbow joint complex. This model is used to determine the complex relationship between EMG and muscle activation, and generates an optimal muscle activation scheme that simulates the actual muscle activation. While the model predicted results of the ballistic movement are very similar, the activation function between the start and the finish is not. This neural network preconditions the signal in an attempt to more closely model the actual activation function over the entire course of the joint movement, and predicts the position, velocity and acceleration around the elbow complex