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Sleep diagnosis by polysomnography is a golden standard but expensive procedure involving huge effort from patients. There remain challenges for smart devices to precisely identify sleep stage and minimize intrusive effect on sleep progression. Herein, a novel noncontact sleep structure prediction system (NSSPS) using a single radar sensor is presented to analyze sleep structure without any tethered unit. The NSSPS is realized through training a convolutional recurrent neural network and neural conditional random fields using reflected radio frequency (RF) waves acquired by radar antennas. By capturing implicit temporal information in RF signals and transitions of sleep progression, high accuracy of sleep\u2010stage prediction is achieved and characteristics of sleep structure are extracted. The performance of the NSSPS is validated by transfer learning between radar signals with different frequency bands and crossvalidation among different subjects. Moreover, the NSSPS is demonstrated to estimate overnight parameters that are critical for sleep diagnosis. Benefiting from its low cost, convenient setup, and accurate prediction capability of sleep\u2010stage identification, the NSSPS can be widely deployed in \u201csmart\u201d homes and exploited to conduct daily sleep structure analysis.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202100227","type":"journal-article","created":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T05:23:40Z","timestamp":1643952220000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Machine Learning\u2010Enabled Noncontact Sleep Structure Prediction"],"prefix":"10.1002","volume":"4","author":[{"given":"Qian","family":"Zhai","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University  Hangzhou 310027 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingyu","family":"Tang","sequence":"additional","affiliation":[{"name":"Respiratory Medicine Department Zhejiang Hospital Lingyin Branch  Hangzhou Zhejiang 310013 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Lu","sequence":"additional","affiliation":[{"name":"Respiratory Medicine Department Zhejiang Hospital Lingyin Branch  Hangzhou Zhejiang 310013 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Respiratory Medicine Department Zhejiang Hospital Lingyin Branch  Hangzhou Zhejiang 310013 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunguang","family":"Li","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Robotics and System of Jiangsu Province School of Mechanical and Electric Engineering Soochow University  Suzhou 215131 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0628-9098","authenticated-orcid":false,"given":"Jingang","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering Rutgers University  Piscataway NJ 08854 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University  Hangzhou 310027 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(19)30198-5"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.5664\/jcsm.3600"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.00226711"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/B0-72-160797-7\/50123-3"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-8986.2010.01030.x"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(15)00051-X"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2612938"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2015.2462812"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2621066"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2228263"},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","unstructured":"F.Adib H.Mao Z.Kabelac D.Katabi R.Miller inACM Conf. 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