Head Direction Command Classification using an Adaptive Optimal Bayesian Neural Network
Huu Thuc Nguyen, S Nguyen Thanh, P. D. Taylor, James Walter Middleton · UTS ePRESS (University of Technology Sydney) · 2007
Mobility has become very important for our quality of life. Head movement is a natural form of pointing and can be used to directly replace the joystick for severely disabled people. In this paper, we describe the development of an optimal Bayesian neural network for the classification of head direction commands in a hands-free wheelchair control system as it allows strong generalisation during the training phase and does not require a validation data set. Experimental results show that with limited training data, an adaptive optimal Bayesian neural network can be developed to classify head direction commands by disabled users with a high sensitivity and specificity of 93.75% and 97.92% respectively.