Abstract:
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.
