We demonstrate an occupancy detection and human activity monitoring system using low-cost motion sensing, edge computing and wireless networking devices. We design a dual Doppler sensor to achieve high signal sensitivity and wide sensing range. We develop signal filtering, detection and machine learning algorithms on an embedded computer to generate classification result in real-time. We also implement web services to enable users to access signal and room state via a wireless network. Compared with conventional occupancy sensors, the dual Doppler system has higher detection rate, and also has the capability of detecting activities even for multiple people.

This paper presents a non-invasive human activity monitoring system with a low-cost Doppler sensor and a pair of radio frequency (RF) sensors. This radio-based system combines the strengths of two sensing modalities: fine granularity from a Doppler sensor and large sensing coverage from an RF link. The system is capable of detecting subtle human motion such as breathing, as well as walking in a large area. We deploy the system and perform experiments in a 5.5 m by 7.5 m room to classify four activities. Experimental results show that the average classification rate is 90%, 31% more accurate than a single Doppler system.