Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer

Sensors

M. Ejaz Ahmed¹ 

[1] Department of Electronics and Radio Engineering, Kyung Hee University, Yongin 446-701, Korea; E-Mail

关键词: MEMS application;    human motion recognition;    non-parametric Bayesian inference;    infinite Gaussian mixture model;    Gibbs sampler;   

DOI  :  10.3390/s121013185

来源: mdpi

【 摘 要 】

In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.