Medusa: Scalable Multi-View Biometric Sensing in the Wild with Distributed MIMO Radars

Yilong Li, Ramanujan K Sheshadri, Karthik Sundaresan, Eugene Chai, Yijing Zeng, Jayaram Raghuram, Suman Banerjee

Abstract: Radio frequency (RF) techniques have shown promise for continuous contactless healthcare applications. However, real-world indoor environments pose challenges for existing systems, which may struggle to detect subtle physiological signals. This paper proposes Medusa, a novel wireless vital-sign sensing system designed for multi-view setups. It enables users to deploy distributed Multiple Input Multiple Output (MIMO) arrays into their daily living environments, facilitating vital-sign sensing in real-world settings. Unlike most existing single Commercial Off-The-Shelf (COTS) radar-based systems that operate under controlled settings Medusa's primary novelty lies in the design of a first-of-its-kind flexible multi-view vital sign sensing system that is view-agnostic, pose-agnostic, contactless, and can sense basic human vitals with good accuracy. Through our well-engineered hardware and software co-design, Medusa enables real-time processing of large distributed MIMO arrays, while balancing the tradeoff between Signal-to-Noise Ratio (SNR) and spatial diversity gain across each of its four distributed 4 × 4 sub-arrays for increased robustness. This is achieved using our novel unsupervised learning model which effectively recovers vital sign waveforms by decomposing the received signals. Extensive evaluations with 21 participants demonstrate Medusa's spatial diversity gain for real-world vital-sign monitoring, enabling free movement and orientation of subjects in both familiar and unfamiliar indoor environments.

External IDs:doi:10.1145/3680207.3723461