Abstract
Pyroelectric infrared (PIR) sensor is widely used as motion detector, due to its sensitivity to the Infrared Radiation Changes (IRC) induced by human motion. In particular, by improving the space resolution and coverage of the PIR sensor, it's possible to determine the human activity patterns using IRC signal. In this paper, we present a novel method of fall detection with PIR sensor network. To improve the space resolution, we use mask arrays as reference structures in the developed IRC sensing modules. A stereo infrared sensing scheme is also designed to enlarge the sensing field for fully capturing the IRC signals. Based on the collected IRC signals, we have performed classification analysis with well known machine learning algorithms. The experimental results demonstrate the effectiveness of the proposed PIR sensor system in fall detection.
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