Improved head direction command classification using an optimised Bayesian neural network.
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.
Nguyen
Son T
ST
Key University Research Centre for Health Technologies, University of Technology, Sydney, NSW, Australia.
Nguyen
Hung T
HT
Taylor
Philip B
PB
Middleton
James
J
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D004867:Equipment Design; D006257:Head / Q000473:pathology*; D006801:Humans; D008328:Man-Machine Systems; D009068:Movement; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012371:Robotics; D012656:Self-Help Devices; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D014910:Wheelchairs