Improvement of Neural Network Learning for Gesture Recognition of Game Contents Using BPN, BPNX, and LM Algorithms
ByungRae Cha, Binod Vaidya, YoungIl Kim · 2009
The purpose of this study was to propose the method to recognize gestures based on inertia sensor which recognizes the movements of the user using inertia sensor, simulated gesture recognition improvement using various neural network learning algorithms (Backpropagation, Fast-Backpropagation, and Levenberg-Marquardt) and lets the user enjoy the game by comparing the recognized movements with the pre-defined movements for the game contents production. Additionally, it was tried to provide users with various data entry methods by letting them wear small controllers using three-axis accelerator sensor. Users can precede the game by moving according to the action list printed on the screen. They can precede the experiential games according to the accuracy and timing of their movements. If they use multiple small wireless controllers together wearing them on the major parts of hands and feet and utilize the proposed methods, they will be more interested in the game and be absorbed in it.