The Effects of Training Set on Prediction of Elbow Trajectory from Shoulder Trajectory during Reaching to Targets

Patients with transhumeral amputations and C5/C6 quadriplegia may be able to use voluntary shoulder motion as command signals for powered prostheses and functional electrical stimulation, respectively. Spatio-temporal synergies exist for goal oriented reaching movements between the shoulder and elbow joints in able bodied subjects. We are using a multi-layer perceptron neural network to discover and embody these synergies. Such a network could be used as a high level functional electrical stimulation (FES) controller that could predict elbow joint kinematics from the voluntary movements of the shoulder joint. Counter-intuitively, a well-chosen reduced data set for training the network resulted in better performance than use of the whole data set against which the predictions of the network were evaluated