Building Occupancy Detection from Carbon-dioxide and Motion Sensors

Abstract

Occupant detection using carbon-dioxide sensors is prevalent but its accuracy is restricted by the inherent sensing delays. This paper proposes an indoor occupant detection method using real-time carbon-dioxide and Pyroelectric Infrared (PIR) sensor measurements overcoming the sensing delays. The occupancy detection problem is formulated as a classification problem wherein the classifier learns from offline carbon-dioxide data and the actual occupancy measurements of the room. While the classifier can provide realtime occupancy detection, the delays in carbon-dioxide sensors influence their accuracy. To overcome the delays, observations from PIR sensors are combined with the results of the single-layer feedforward neural network (SLFN) based classifier. The classifier works in four steps: (i) data-preprocessing, (ii) feature-selection, (iii) learning, and (iv) validation. The data is preprocessed by smoothing and several features are selected as input to the SLFN. Then, the classifier is validated with realtime experiments. Our results demonstrate that the proposed approach provides accuracy up to 99.79% and also overcomes the delays found in carbon-dioxide sensors.