RestAware: Non-Invasive Sleep Monitoring Using FMCW Radar and AI-Generated Summaries

TL;DR

RestAware is a non-invasive sleep monitoring system using FMCW radar and AI to classify sleep postures with high accuracy and generate personalized summaries, offering a privacy-preserving alternative to traditional methods.

Contribution

The paper introduces a contactless sleep monitoring system with high accuracy and integrates AI-generated summaries, advancing privacy-preserving sleep analysis technology.

Findings

92% classification accuracy on sleep postures

F1-score of 0.91 with KNN classifier

Effective AI-generated personalized sleep summaries

Abstract

Monitoring sleep posture and behavior is critical for diagnosing sleep disorders and improving overall sleep quality. However, traditional approaches, such as wearable devices, cameras, and pressure sensors, often compromise user comfort, fail under obstructions like blankets, and raise privacy concerns. To overcome these limitations, we present RestAware, a non-invasive, contactless sleep monitoring system based on a 24GHz frequency-modulated continuous wave (FMCW) radar. Our system is evaluated on 25 participants across eight common sleep postures, achieving 92% classification accuracy and an F1-score of 0.91 using a K-Nearest Neighbors (KNN) classifier. In addition, we integrate instruction-tuned large language models (Mistral, Llama, and Falcon) to generate personalized, human-readable sleep summaries from radar-derived posture data. This low-cost ($ 35), privacy-preserving solution offers a practical alternative for real-time deployment in smart homes and clinical environments.