Bayesian neural network classification of head movement direction using various advanced optimisation training algorithms

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Conference Proceeding
Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006, 2006, 2006 pp. 1014 - 1019
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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.
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