RAM: Radar-based Activity Monitor
Published in PerCom, 2024
Md Abdullah Al Hafiz Khan, Ruthvik Kukkapalli, Piyush Waradpande, Sekar Kulandaivel, Nilanjan Banerjee, Nirmalya Roy, Ryan Robucci. In Proceeding of InfoCom, San francisco, California, April, 2016.
Abstract:
Activity recognition has applications in a variety of human-in-the-loop settings such as smart home health monitoring, green building energy and occupancy management, intelligent transportation, and participatory sensing. While fine-grained activity recognition systems and approaches help enable a multitude of novel applications, discovering them with non-intrusive ambient sensor systems pose challenging design, as well as data processing, mining, and activity recognition issues. In this paper, we develop a low-cost heterogeneous Radar based Activity Monitoring (RAM) system for recognizing fine-grained activities. We exploit the feasibility of using an array of heterogeneous microdoppler radars to recognize low-level activities. We prototype a short-range and a long-range radar system and evaluate the feasibility of using the system for fine-grained activity recognition. In our evaluation, using real data traces, we show that our system can detect fine-grained user activities with 92.84% accuracy.