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However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.<\/jats:p>","DOI":"10.1145\/3478090","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T22:48:23Z","timestamp":1631659703000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":69,"title":["RF Vital Sign Sensing under Free Body Movement"],"prefix":"10.1145","volume":"5","author":[{"given":"Jian","family":"Gong","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, China"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of California San Diego, United States"}]},{"given":"Kaixin","family":"Lin","sequence":"additional","affiliation":[{"name":"University of California San Diego, United States"}]},{"given":"Ju","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, BNRist, Tsinghua University, China, School of Computer Science and Engineering, Central South University, ChangSha, China"}]},{"given":"Yaoxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, BNRist, Tsinghua University, China, School of Computer Science and Engineering, Central South University, ChangSha, China"}]},{"given":"Wenxun","family":"Qiu","sequence":"additional","affiliation":[{"name":"Samsung Research America, Plano, Texas, United States"}]}],"member":"320","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram. 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