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Internet Things"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            In this work, we propose\n            <jats:italic>MiSleep<\/jats:italic>\n            , a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems,\n            <jats:italic>MiSleep<\/jats:italic>\n            is not privacy-invasive and does not require users to wear anything on their body.\n            <jats:italic>MiSleep<\/jats:italic>\n            leverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging and specularity in signals.\n            <jats:italic>MiSleep<\/jats:italic>\n            builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction,\n            <jats:italic>MiSleep<\/jats:italic>\n            designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn and predict the posture only during the rest states.\n            <jats:styled-content style=\"color:#000000\">\n              We evaluate\n              <jats:italic>MiSleep<\/jats:italic>\n              with real data collected from Commercial-Off-The-Shelf mmWave devices for eight volunteers of diverse ages, genders, and heights performing different sleep postures\n            <\/jats:styled-content>\n            .\n            <jats:styled-content style=\"color:#000000\">\n              We observe that\n              <jats:italic>MiSleep<\/jats:italic>\n              identifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.\n            <\/jats:styled-content>\n          <\/jats:p>","DOI":"10.1145\/3643866","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T11:55:20Z","timestamp":1706788520000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["MiSleep: Human Sleep Posture Identification from Deep Learning Augmented Millimeter-wave Wireless Systems"],"prefix":"10.1145","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2747-2764","authenticated-orcid":false,"given":"Aakriti","family":"Adhikari","sequence":"first","affiliation":[{"name":"University of South Carolina, Columbia, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5711-3087","authenticated-orcid":false,"given":"Sanjib","family":"Sur","sequence":"additional","affiliation":[{"name":"University of South Carolina, Columbia, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Lisa Matricciani Yu Sun Bin Tea Lallukka Erkki Kronholm Melissa Wake Catherine Paquet Dorothea Dumuid and Tim Olds. 2018. 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