Abstract:
Assistive technologies have recently emerged to
improve the quality of life of severely disabled people by
enhancing their independence in daily activities. Since many of
those individuals have limited or non-existing control from the
neck downward, alternative hands-free input modalities have
become very important for these people to access assistive
devices. In hands-free control, head movement has been proved
to be a very effective user interface as it can provide a
comfortable, reliable and natural way to access the device.
Recently, neural networks have been shown to be useful not
only for real-time pattern recognition but also for creating
user-adaptive models. Since multi-layer perceptron neural
networks trained using standard back-propagation may cause
poor generalisation, the Bayesian technique has been proposed
to improve the generalisation and robustness of these networks.
This paper describes the use of Bayesian neural networks in
developing a hands-free wheelchair control system. The
experimental results show that with the optimised architecture,
classification Bayesian neural networks can detect head
commands of wheelchair users accurately irrespective to their
levels of injuries.
