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.
