Occupant Detection and Tracking in Smart Buildings using Unobtrusive Sensing

Non-intrusive occupant identification enables numerous applications in smart buildings such device-free climate control and adjustment in buildings which would not only enhance occupants' comfort but also save energy. To achieve that, we propose a set of methods to identify occupants by sensing their body shape, movement, behaviors, and the path taken as they walk through the door or a network of doors.

We mount three ultrasonic ping sensors, one on top to sense height and two on the sides of the door to sense width. We extract a feature set from the occupant's walk to non-intrusively identify him. We cluster the occupants using their waist girth and the time spent in the doorway. We are able to identify 20 occupants with an accuracy of 95%. Although this technology is four times better than the state of the art, it is not applicable to realistic building settings where the number of occupants is larger than 20.

To achieve building-level occupant identification, we propose a set of improvements to the sensing platform which led to increasing the sampling rate from 30 Hz to over 132 Hz by performing sensor-sampling optimizations and sampling parallelization. We propose a novel methodology to filter down possible candidates and model the paths users take in the building using a Markov Model rather than relying on our clustering algorithm to point to the identity of the user. We also develop algorithms that detect a set of "behaviors" that users perform as they walk through the door. Such behaviors skew the data making the user appear as a different person and leads to misidentification. We accurately identified 100 people in a building using a network of five doors. This system is useful because it makes buildings aware of their users and their preferences which can lead to increased comfort and energy savings of the around 10% beyond regular occupancy-based energy saving systems.