Abstract :
The recognition of hand gestures is a challenging task for the high degrees of freedom of hand motion. We develop a virtual reality based driving training system of self-propelled gun (SPG). For this system, a dataglove with 18 sensors is employed to perform some driving tasks such as pressing switches, manipulating steering wheel, changing gears, etc. To accomplish these tasks, some hand gestures must be defined from the dataglove sensors data. A feedforward neural network can represent an arbitrary functional mapping so it is possible to map raw data directly to the required hand gestures. This paper uses BP neural network to recognize the hand patterns which exist in the raw sensor data of the dataglove. A pattern set of 300 hand gestures is used to train and test the neural network. The recognition system achieves good performance. It can be effectively used in our virtual reality training system of SPG to perform various manipulating tasks in a more fast, precise, and natural way
Keywords :
backpropagation; computer based training; feedforward neural nets; gesture recognition; image motion analysis; sensor fusion; traffic engineering computing; virtual reality; BP neural network; arbitrary functional mapping; dataglove; feedforward neural network; hand gesture recognition; neural network training; self-propelled gun; virtual reality based driving training system; Data gloves; Feedforward neural networks; Gears; Neural networks; Pattern recognition; Pressing; Sensor systems; Switches; Virtual reality; Wheels;
