Toward Scalable Activity Recognition for Sensor Networks

Sensor networks hold the promise of truly intelligent buildings: buildings that adapt to the behavior of their occupants to improve productivity, efficiency, safety, and security. To be practical, such a network must be economical to manufacture, install and maintain. Similarly, the methodology must be efficient and must scale well to very large spaces. Finally, be be widely acceptable, it must be inherently privacy-sensitive. We propose to address these requirements by employing networks of passive infrared (PIR) motion detectors. PIR sensors are inexpensive, reliable, and require very little bandwidth. They also protect privacy since they are neither capable of directly identifying individuals nor of capturing identifiable imagery or audio. However, with an appropriate analysis methodology, we show that they are capable of providing useful contextual information. The methodology we propose supports scalability by adopting a hierarchical framework that splits computation into localized, distributed tasks. To support our methodology we provide theoretical justification for the method that grounds it in the action recognition literature. We also present quantitative results on a dataset that we have recorded from a 400 square meter wing of our laboratory. Specifically, we report quantitative results that show better than 90% recognition performance for low-level activities such as walking, loitering, and turning. We also present experimental results for mid-level activities such as visiting and meeting.