Dynamic Gesture Recognition based on LeapMotion and HMM-CART Model

Abstract

This paper focuses on improving the recognition accuracy of dynamic gesture learning, and proposes dynamic gesture track recognition strategy based on HMM-CART model structure. It integrates the modeling ability of the HMM (Hidden-Markov-Model) for time series data and the interpretability of CART (Classification-And-Regression-Tree) for its ability of fast classification and regression, and use it for dynamic gesture recognition. Time series data of movement gestures collected by LeapMotion sensor will be first divided into four channels: finger shape, palm normal vector, palm ball radius and palm displacement vector, and then HMM in HMM Layer will be built for each channel, finally the likelihood probability of model calculated for each sub-sequence of observation should be classified as the input of the CART model in CART Layer to identify the gesture. Experiments show that the accuracy of dynamic gesture recognition method using HMM-CART model is higher than that of traditional single channel HMM.