Motion recognition based on Dynamic-Time Warping method with Self-Organizing Incremental Neural Network

This paper presents an approach (SOINN-DTW)for recognition of motion (gesture) that is based on the Self-Organizing Incremental Neural Network (SOINN) and Dynamic Time Warping (DTW). Using SOlNN´s function of eliminating noise in the input data and representing the distribution of input data, SOINN-PTW method approximates the output distribution of each state in a self-organizing manner corresponding to the input data. The proposed SOINN-DTW method enhances Stochastic Dynamic Time Warping Method (Nakagawa. 1986). Results of experiments show that SOINN-DTW outperforms HMM, CRF. and HCRF in motion data.