A Motion Induced Passive Infrared (PIR) Sensor for Stationary Human Occupancy Detection

Abstract

Passive Infrared (PIR) sensors are commonly used in indoor applications to detect human presence. PIR sensors detect human presence by detecting the change in infrared radiation across the polarity of the sensor. Due to this, PIR sensors are unable to accurately detect stationary human subjects, which results in false negatives. In the pursuit of creating a low-cost solution for detecting stationary occupants in a closed space, the novel approach to mount a PIR sensor on a moving platform was developed (MI-PIR). This approach was developed for the system to artificially induce the motion that is necessary for stationary human detection. Utilizing the raw analog output of the PIR sensor and an artificial neural network (ANN), the closed space was accurately classified for room occupancy, the number of occupants, the approximate location of the human targets, and the differentiation of targets. This novel approach provides the advantages of a utilizing a single PIR sensor for human presence detection, while eliminating the major known drawback to this type of sensor. Scanning the room using a PIR sensor also allows for an expanded field of view (FoV) and a simpler deployment, in comparison to other approaches using a PIR sensor. Finally, MI-PIR expands the functionalities of PIR sensors by using an ANN to detect various other occupancy parameters. The experimental results show that the system can detect room classification with 99% accuracy, 91% accuracy in occupancy count estimation, 93% accuracy in relative location prediction, and 93% accuracy in human target differentiation. These results show promise for an application of tracking and monitoring an at-risk patient in an indoor setting.