Hand trajectory-based gesture spotting and recognition using HMM
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Efficacy of gesture for communication among humanoid robots by fuzzy inference method
International Journal of Computational Vision and Robotics
Gesture recognition through HITEG data glove to provide a new way of communication
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Real-time hand gesture recognition using complex-valued neural network (CVNN)
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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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.