Bayesian Neural Network Classification of Head Movement Direction using Various Advanced Optimisation Training Algorithms * Son T. Nguyen Faculty of Engineering University of Technology, Sydney Broadway NSW 2007 Australia Son.NguyenThanh@uts.edu.au Hung T. Nguyen Faculty of Engineering University of Technology, Sydney Broadway NSW 2007 Australia Hung.Nguyen@uts.edu.au Philip B. Taylor Faculty of Engineering University of Technology, Sydney Broadway NSW 2007 Australia Philip.Taylor@uts.edu.au * This work was supported by an ARC LIEF grant (LE0454081). Abstract - Head movement is one of the most effective hands-free control modes for powered wheelchairs. It provides the necessary mobility assistance to severely disabled people and can be used to replace the joystick directly. In this paper, we describe the development of Bayesian neural networks for the classification of head movement commands in a hands-free wheelchair control system. Bayesian neural networks allow strong generalisation of head movement classifications during the training phase and do not require a validation data set. Various advanced optimisation training algorithms are explored. Experimental results show that Bayesian neural networks can be developed to classify head movement commands by abled and disabled people accurately with limited training data. Index Terms - Bayesian neural networks; head-movement classification; powered wheelchair. I. INTRODUCTION The use of joysticks as a form of control for severely disabled people is a very demanding task. These people have severe mobility disabilities such as cerebral palsy, tetraplegia, etc. Head movement is one of the most effective hands-free modes for the control of powered wheelchairs as it provides the necessary mobility assistance. Currently, it remains a major and elusive task to develop an innovative head movement interface for a human-machine system which assures the safety of the operator, remains unobtrusive, is easy to learn and can easily be adapted to a new operator with different mobility impairments. Many people with spinal cord injury who use head controls have very poor control of the musculature supporting the upper body and neck, and consequently control of head position is marginal at the best of times. Other challenges arise from changed position of the body in relation to the wheelchair. A feed-forward neural network classically trained using back-propagation can be viewed an effective classifier for head movements of severely disabled people [1], [2], [3]. However, the main disadvantage of standard neural networks is the potential of poor generalisation when facing with limited training data. Recently, Bayesian techniques have been applied to neural networks to improve the accuracy and robustness of neural network classifiers. In our previous research [4], it was shown that Bayesian neural networks could be used to classify head movement commands consistently with limited training data. In this paper, we continue to explore the properties of Bayesian neural networks in a hands-free control wheelchair control system using various advanced optimisation training algorithms. In the near future, the optimal Bayesian neural network will provide the ability for the system to be trained on-line for each individual operator, irrespective of his/her disability. Section II describes the formulation of Bayesian neural network classification. In Section III, we briefly discuss various advanced optimisation algorithms used for the training of Bayesian neural networks. Section IV shows experimental results of head movement classifications in a hands-free wheelchair control system. Section V provides a discussion of these results and directions for the future development of the system. II. BAYESIAN NEURAL NETWORK CLASSIFICATION Bayesian neural networks were firstly introduced by MacKay [5], [6], [7]. A Bayesian neural network has the following main benefits compared to a standard neural network: Its network training adjusts weight decay parameters automatically to optimal values for the best generalisation. The adjustment is done during training, so the computational intensive search for the weight decay parameters is no longer required. Its training converges to different local minima and networks with different numbers of hidden nodes can be compared and ranked. As no separate validation set is required, all available data can be used for training. A. Multi-Layer Perceptron Neural Networks Multi-layer perceptron (MLP) neural networks are widely used in engineering applications. These networks take in a vector of real inputs, i x , and from them compute one or more values of the output layer, w x z k , . With a one hidden layer network, as shown in Fig 1, the value of the k th output is computed as follows: