A tapping interface for finger movement training using magnetic sensors

This paper proposes a novel human interface that can be used to operate domestic appliances and game machines. In this system, finger tapping movement is measured by magnetic sensors, and is evaluated by computing five features, such as the finger tapping interval, on a real-time basis. These factors are then discriminated through a probabilistic neural network and allocated as machine operation commands. The user can thus voluntarily operate various machines using finger tapping movements. Experimental results showed that the prototype system developed can learn and classify user movements with a high degree of accuracy (average rates: 98.56plusmn1.15 [%]), and that it can be used to smoothly control appliances and game machines. The possibility of the system supporting finger movement training was also confirmed with three subjects through game operation.