RestAware: Non-Invasive Sleep Monitoring Using FMCW Radar and AI-Generated Summaries
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918111408553984 |
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| author | Banerjee, Agniva Paregi, Bhanu Partap Lone, Haroon R. |
| author_facet | Banerjee, Agniva Paregi, Bhanu Partap Lone, Haroon R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RestAware: Non-Invasive Sleep Monitoring Using FMCW Radar and AI-Generated Summaries Banerjee, Agniva Paregi, Bhanu Partap Lone, Haroon R. Human-Computer Interaction Computers and Society Signal Processing 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. |
| title | RestAware: Non-Invasive Sleep Monitoring Using FMCW Radar and AI-Generated Summaries |
| topic | Human-Computer Interaction Computers and Society Signal Processing |
| url | https://arxiv.org/abs/2508.00848 |