Multichannel Human Motion Similarity Analysis Based on Information Entropy and Dynamic Time Warping

Abstract:

Evaluating motion similarity between trainer and trainee is a key part in computer-assisted sports teaching system. Our similarity evaluation algorithm mainly contains four steps. Firstly, the multichannel 3D human motion data are captured using the Kinect, a depth sensor of Microsoft. Next, in order to greatly reduce the amount of data analysis, the piecewise extremum method (PEM) is applied to achieve this goal. Then, considering that doing the same motions the rhythms of different people are not synchronized, the Dynamic Time Warping algorithm (DTW) is selected to solve the problem of analyzing one channel unequal length motion sequences. Finally, the similarity between the two sets of multichannel human motion sequences can be evaluated using the combined method of the information entropy and DTW. The experimental results indicate that compared with other traditional methods, the proposed method not only accurately measures similarity degree of different motions, but also requires less computational time and memory storage capacity.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 847-851
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.847
• Online since: November 2014
• Authors: Ming Zeng*, Zai Xin Yang, Hong Lin Ren, Qing Hao Meng
• Keywords: DTW, Information Entropy, Motion Similarity, Piecewise Extremum Method (PEM)

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