Occu-track: occupant presence sensing and trajectory detection using non-intrusive sensors in buildings

Abstract

Sensing occupant presence and their trajectories of movement in buildings enable new types of analysis and building operation strategies. However, obtaining such information in a cost-efficient and non-intrusive manner is a challenge. This paper proposes the Occu-track method for how inexpensive battery-powered sensors can be used at scale to estimate occupant presence and movement trajectories. The technique combines graph analysis and advanced clustering to produce accurate estimates. This paper validates the efficiency of Occu-track in two different settings; a music room and a private office. The experimental results from two room-level deployments demonstrate the benefits of the approach obtaining an average Root Mean Squared Error of 1.19 meters for case 1 and 0.88 meters for case 2 for trajectory estimation. The results can contribute to new dimensions of research associated with the generation of metadata from non-intrusive sensors to make informed decisions about efficient space utilization and floor plans, intelligent building operations, crowd management, comfortable indoor environment, or managing personnel.