Performance of a head-movement interface for wheelchair control

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.