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Sensors (Basel). 2016 Jun 3;16(6). pii: E822. doi: 10.3390/s16060822.

Abnormal Activity Detection Using Pyroelectric Infrared Sensors.

Author information

1
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, China. woodwood2000@163.com.
2
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, China. tanhuoyuan@gmail.com.
3
College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture Engineering, Guangzhou 5102256, China. qiuju95@126.com.
4
Department of Electronic Science, Huizhou University, Huizhou 516007, China. liutongboss@163.com.
5
School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China. zhuohank@mail.sysu.edu.cn.
6
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China. bhshen@sohu.com.

Abstract

Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process.

KEYWORDS:

abnormal activity detection; pyroelectric infrared (PIR) sensor; wireless sensor network

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