Advanced bayesian neural network classifiers of head-movement directions for severely disabled people
- Publication Type:
- Thesis
- Issue Date:
- 2006
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Assistive technologies have been dedicated to providing additional accessibility to
individuals who have physical or cognitive difficulties, impairments and disabilities,
Various types of assistive technology products are also available on the market today.
However, there are still a significant number of disabled people who are unable to use
commercial assistive devices due to their high level of injury.
For severely disabled people with quadriplegia resulting from high-level spinal-cord
injuries or cerebral palsy, hands-free control methods have become extremely beneficial
for them to reducing their dependence level in daily activities. In this control mode,
head movement has been shown to be a very effective, natural and comfortable way to
access the device. The need to exactly detect intentional head movements of various
forms of disabled people has led to the use of neural networks. Recently, Bayesian
neural networks have been proposed for developing neural network applications with
finite and/or noisy training data.
In assistive technologies, power wheelchairs are a means of providing independent
mobility. This thesis explores the useful properties of Bayesian neural networks in
developing an optimal head movement-user interface for hands-free power wheelchair
control systems. In such systems, a trainable Bayesian neural network is used to detect
head movement commands. This kind of user interface can conveniently be used by
various disabled users. The thesis also proposes the techniques for developing the
adaptive Bayesian neural network for head movement classification, including the
determination of the optimal architecture and the most effective on-line training
algorithm for the network.
The experimental results obtained in the thesis show that a Bayesian neural network can
be used to detect head movements accurately and consistently. After on-line training,
the network is able to adapt well to the head movements of new users. The substantial
contributions of the thesis can briefly be summarised as follows:
• Standard methods of neural network training usually require intensive search
for network parameters or require the use of a validation set separated from the
available training data. In contrast, in this thesis all the available training data
have been used to train the network for detecting head movements. As the
network could be trained on all the data from a group of different individuals, it
is able to classify their new head movements with a very high accuracy, of
99.38%.
• The thesis strongly focuses on advanced training algorithms suitable for
Bayesian neural network head-movement classifiers. Especially, the quasiNewton
training and scaled conjugate gradient algorithms have been found to
be the most effective, as they can result in the shortest training time for the
network.
• In general, the determination of the best network architecture is very difficult
and is traditionally based on ad hoc methods. However, this thesis utilises the
property of the maximum evidence, which is available in Bayesian neural
networks, to select the optimal network architecture. Specifically, the networks
containing three hidden neurons are appropriate for successful head-movement
classification.
• The thesis also provides a novel method of early stopping in neural networks.
In this method, the maximum evidence can be seen as a good criterion to
terminate the training process before the network overfits the data. In addition,
the use of a validation set for monitoring the generalisation error is no longer
needed. Moreover, this thesis shows that the combination of independent
Bayesian neural networks can significantly improve the head-movement
classification accuracy.
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