Peripheral WiFi Vision: Exploiting Multipath Reflections for More Sensitive Human Sensing

A large amount of energy could be saved by detecting home occupancy and automatically controlling the lights, and HVAC. Existing occupancy sensors can detect the motion of people but cannot detect people when they are stationary. In this paper, we present a system called Peripheral WiFi Vision (PeriFi), which exploits multipath reflections as individual spatial sensors to increase the sensitivity of the conventional approaches. PeriFi analyzes each multipath component independently, increasing sensitivity so it can directly sense both moving and non-moving occupants. Our evaluations for 6 physical configurations with 11 different occupancy states show that PeriFi can achieve 96.7% accuracy, which translates to nearly 30% improvement over the conventional approaches.