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.
Highlights
To avoid the laborious and inconsistent manual data labeling process, we propose using the self-tuning spectral clustering algorithm to discover the number of normal activities automatically
By employing the Field of View (FOV) modulation for Pyroelectric Infrared (PIR) sensors, the human activity is encoded into low-dimensional data streams, which can be used to extract the tempo-spatial feature of the human motion
A major advantage of our approach is that it does not need abnormal samples for training in advance. This is critical in real deployment because it is unrealistic to train the system by providing all kinds of abnormal activities. Another advantage is that our training procedure is an unsupervised learning fashion, which does not need to specify the number of kinds of normal activities
Summary
Sensors 2016, 16, 822 such as computational complexity in image processing, data consistency under different illumination conditions, and privacy infringement of the human target [8]. These problems make the practical deployment of vision-based systems difficult. An alternative method is to collect sensing data from wearable motion sensors and detect abnormal activities based on the collected sensing data [2]. Robust to the change of environment, especially the light illumination; protective to the residents’ privacy; convenient to use, especially for the elderly Bearing those factors in mind, a Pyroelectric Infrared (PIR) sensing paradigm offers a promising alternative to the optical and wearable counterparts [3].
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