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Dmitri De Vaz

Detecting Presence Using Machine Learning

Objective #

The goal was to improve the accuracy and reliability of detecting whether or not occupants are present in a space using a ceiling mounted sensor cluster. The idea was to combine motion sensing (PIR) and sound (MEMS microphones) to detect presence instead of just detecting motion.

To make the distinction clear, traditional motion sensing uses PIR sensors to detect whether there is motion in the space. Motion sensors work by detecting the IR energy as a warm foreground object (such as a person) moves through an background environment. This requires the foreground object to be in motion, since a voltage is generated in proportion to rate of IR change across multiple IR detectors in the sensor (pyroelectric effect). If the foreground object remains stationary (eg. occupant seated at desk and not moving much), the motion sensor will not activate. If the lighting in the room is triggered based on motion sensors (as is very commonly the case for energy savings purposes) then the lights often annoyingly turn off even though there may be occupants in the room - requiring occupants to wave their arms to generate enough motion to turn the lights back on.

To solve this problem, we needed to detect the presence of occupants and not just their motion.

My Role #

As the senior machine learning engineer, my role was to design, develop, and deploy this feature in collaboration with the product software team. I was responsible for:

Impact #