Hand Gesture Recognition Based on Multi-Classification Adaptive Neuro-Fuzzy Inference System and pMMG

In this paper, a multi-classification adaptive neuro-fuzzy inference system combining neural-network and a TSK fuzzy system is proposed to recognize six commonly used gestures. Several techniques including mini-batch gradient descent with L2 regularization, DropRule and AdaBound are integrated to improve the generalization ability of the system and the efficiency of training. Numerical results show that the average classification accuracy of the multiple classifier systems is 95.12%, and this value is higher than some other multiple classifier systems (CNN, LDA, etc.) using MMG signals as the inputs for hand gesture recognition.