Classifying motion time series using neural networks

Authors:
Lidan Shou;Ge Gao;Gang Chen;Jinxiang Dong
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China
Venue:
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Year:
2006

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Abstract

This paper proposes an effective time-series classification model based on the Neural Networks. Classification under this model consists of three phases, namely data preprocessing, training, and testing. The main contributions of the paper are described as following: We propose a feature extraction algorithm, which involves computation of finite difference of sequences, for preprocessing. We employ two different types of Neural Networks for training and testing. The results of the experiments on real univariate motion capture data and synthetic data show that our approach is effective in providing good performance in terms of accuracy. It is therefore a promising method for classifying time-series, in particular for univariate human motion capture data.