Room Occupancy Determination Using Multimodal Sensor Fusion

In home/office automation applications, pyroelectric infrared (PIR) sensors have been widely used for human presence detection. However, PIR sensors suffer from false-on and false-off problems. In this study, we used multimodal sensors to complement each other in order to improve the detection performance. In addition, we proposed a low-computational-complexity sensor fusion algorithm to infer the status of room occupancy, which is very suitable for manipulation using the sensor nodes of wireless sensor networks. By combining spatial and temporal data through a sensor fusion mechanism, the proposed method can address the missing sensing values problem of PIR sensors, thus improving the accuracy of room occupancy determination. The inference algorithm of sensor fusion was evaluated for the sensor detection accuracy and compared with multisensor fusion using dynamic Bayesian networks (DBNs). The experimental results showed that the detection accuracy of room occupancy was greater than 99%, which was better than that of the DBN-based sensor fusion method.