Abstract:
Head movement has been used as a control
interface for people with motor impairments in a range of
applications. Chin operated joysticks and switch arrays have
been incorporated in control systems for electric wheelchairs
but have several disadvantages, including being difficult to
operate and aesthetically unattractive. A prototype wheelchair
control interface has been developed that makes use of an
artificial neural network (ANN) to recognize commands given
by head movement. This paper presents the results of an
experimental investigation of the ANN's performance in terms
of classification accuracy and delay. It goes on to compare the
results of disabled with able-bodied users, and assesses the
effect of providing real-time feedback to the user.
The results obtained indicate that ANN techniques can be
used to classify head movements sufficiently quickly and
accurately to be used in a practical interface. The provision of
graphical real-time feedback does not appear to be crucial, but
may be of benefit for particular cases.
