标题： RestAware：使用FMCW雷达和AI生成摘要的非侵入式睡眠监测 显示英文标题

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

摘要： 监测睡眠姿势和行为对于诊断睡眠障碍和提高整体睡眠质量至关重要。 然而，传统的方法，如可穿戴设备、摄像头和压力传感器，通常会降低用户的舒适度，在被毯子等遮挡物下无法正常工作，并引发隐私问题。 为了克服这些限制，我们提出了RestAware，这是一种基于24GHz调频连续波（FMCW）雷达的非侵入性、无接触的睡眠监测系统。 我们的系统在8种常见的睡眠姿势上对25名参与者进行了评估，使用K-近邻（KNN）分类器实现了92%的分类准确率和0.91的F1分数。 此外，我们整合了经过指令微调的大语言模型（Mistral、Llama和Falcon），从雷达衍生的姿势数据中生成个性化的人类可读睡眠总结。 这种低成本（35美元）、保护隐私的解决方案为智能家庭和临床环境中的实时部署提供了一个实用的选择。 显示英文摘要

摘要： 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.