Prediction of the Thumb Finger Extensor and Flexor Muscles Desired Activation Pattern during Hand Writing and Painting Using Neural Networks

Multiple muscle groups may be activated simultaneously during the most of activities. So, the appropriate muscle coordination must be emerged during a normal activity. Consequaently, for rehabilitation of movements such as hand writing and paiting in patients for example suffering from carpal channel syndrom or incomplete spinal cord injury, the correct muscle coordination patterns between the finger muscles and wrist muscles must be reestablished. So, in this paper a prediction methodology based on artificial neural networks (ANN) is proposed to approximate the Thumb fingure extensor and flexor muscles desired activation pattern during the hand writing and Painting. In the presented strategy, A nonlinear auto-regressive neural network (NARX), Recurrent Neural Network (RNN), Radial Basis Function (RBF), Multy Layer Perceptron (MLP) and an Adaptive-network-based fuzzy inference system (ANFIS) are trained to forecast the Extensor pollicis longus and Flexor pollicis brevis muscles activity of one thumb finger of hand using Extensor carpi radialis brevis and Flexor carpi ulnaris muscles activity of forearm. Quantitative evaluations show the promising performance of developed neural networks. Eight healthy volunteers participated in the experiments.