Abstract
Pyroelectric infrared (PIR) sensors are widely used as a presence trigger, but the analog output of PIR sensors depends on several other aspects, including the distance of the body from the PIR sensor, the direction and speed of movement, the body shape and gait. In this paper, we present an empirical study of human movement detection and idengification using a set of PIR sensors. We have developed a data collection module having two pairs of PIR sensors orthogonally aligned and modified Fresnel lenses. We have placed three PIR-based modules in a hallway for monitoring people; one module on the ceiling; two modules on opposite walls facing each other. We have collected a data set from eight subjects when walking in three different conditions: two directions (back and forth), three distance intervals (close to one wall sensor, in the middle, close to the other wall sensor) and three speed levels (slow, moderate, fast). We have used two types of feature sets: a raw data set and a reduced feature set composed of amplitude and time to peaks; and passage duration extracted from each PIR sensor. We have performed classification analysis with well-known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from a single PIR sensor of each of the three modules, we could achieve more than 92% accuracy in classifying the direction and speed of movement, the distance interval and idengifying subjects. We could also achieve more than 94% accuracy in classifying the direction, speed and distance and idengifying subjects using the reduced feature set extracted from two pairs of PIR sensors of each of the three modules.
Highlights
With the advancement of sensor and actuator technologies, our indoor environment, such as buildings, has been instrumented with various sensors, including temperature, humidity, illumination, CO2 and occupancy sensor, and, can be aware of changes in the user’s state and surrounding, controlling building utilities to adapt their services and resources to the user’s context, e.g., automatic lighting control, heating, ventilation and air-conditioning (HVAC) system adjustment, electrical outlet turn-off, unusual behavior detection and home invasion prevention
We could achieve more than 94% accuracy in classifying the direction, speed level and distance interval and identifying walking subjects using the reduced feature set extracted from each of the three modules equipped with two pairs of Pyroelectric infrared (PIR) sensors
We have presented a human movement detecting system based on pyroelectric infrared (PIR) sensors and machine learning technologies for classifying the direction of movement, the distance of the body from the PIR sensors, the speed of movement during two-way, back-and-forth walking and identifying the walking subject
Summary
With the advancement of sensor and actuator technologies, our indoor environment, such as buildings, has been instrumented with various sensors, including temperature, humidity, illumination, CO2 and occupancy sensor, and, can be aware of changes in the user’s state and surrounding, controlling building utilities to adapt their services and resources to the user’s context, e.g., automatic lighting control, heating, ventilation and air-conditioning (HVAC) system adjustment, electrical outlet turn-off, unusual behavior detection and home invasion prevention. Such context-aware systems have deployed occupant location as the principal form of the user’s context. We can leverage discriminative features of the analog output signal of PIR sensors in order to develop various applications for indoor human tracking and localization
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