Unobtrusive Occupancy Detection with FastGRNN on Resource-Constrained BLE Devices

Abstract

The emerging area of device-free occupancy detection (DfOD) has seen slow adoption due to deployability, scalability, and energy efficiency concerns resulting from the use of large, costly, and power-hungry devices like laptops and WiFi routers in the state-of-the-art solutions. Moreover, these approaches often rely on cloud-offloading for data processing which requires extra communication latency and energy. To overcome these challenges, we develop an RF-based DfOD system using easily-deployable Bluetooth Low Energy (BLE) devices. Our system uses a kilobyte-sized machine learning algorithm running on the BLE device to predict the occupancy of a room from a small number of wireless packets, thereby enabling energy-frugal realtime analytics. We validate our approach with experiments in two indoor rooms using four nRF52840 BLE radios. Initial results suggest our system can detect occupancy of an indoor environment with 95% accuracy, 96% precision, and 92% recall while drawing a meager amount of current.