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Abstract : |
Instrumented gloves use a variety of sensors to provide information about the user's hand. They can be used for recognition of gestures; especially well-defined gesture sets such as sign languages. However, recognising gestures is a difficult task, due to intrapersonal and interpersonal variations in performing them. One approach to solving this problem is to use machine learning. In this case, samples of 95 discrete Australian Sign Language (Auslan) signs were collected using a PowerGlove. Two machine learning techniques were applied-- instance-based learning (IBL) and decision-tree learning-- to the data after some simple features were extracted. Accuracy of approximately 80 per cent was achieved using IBL, despite the severe limitations of the glove., |
