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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated and untimely. The emergence of mobile devices provides the possibility to manage people\u2019s health status in a convenient and instant way. Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging. We propose to model the behavior-related multi-source data streams with a local-global graph, which contains multiple local context sub-graphs to learn short-term local context information with heterogeneous graph neural networks and a global temporal sub-graph to learn long-term dependency with self-attention networks. Then health status is predicted based on the structure-aware representation learned from the local-global behavior graph. We take experiments on the StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.<\/jats:p>","DOI":"10.1145\/3457893","type":"journal-article","created":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T21:16:06Z","timestamp":1636751766000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Health Status Prediction with Local-Global Heterogeneous Behavior Graph"],"prefix":"10.1145","volume":"17","author":[{"given":"Xuan","family":"Ma","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; and Peng Cheng Laboratory, Shenzhen, China"}]},{"given":"Xiaoshan","family":"Yang","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; and Peng Cheng Laboratory, Shenzhen, China"}]},{"given":"Junyu","family":"Gao","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; and Peng Cheng Laboratory, Shenzhen, China"}]},{"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; and Peng Cheng Laboratory, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"World Health Organization. 2009. https:\/\/www.who.int\/mediacentre\/multimedia\/podcasts\/2009\/lifestyle-interventions-20090109\/en\/."},{"key":"e_1_3_1_3_2","unstructured":"2017. http:\/\/www.sdwsnews.com.cn\/a\/jiankangtuku\/2017\/1108\/15306.html."},{"key":"e_1_3_1_4_2","unstructured":"World Health Organization. 2020. https:\/\/www.who.int\/."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.09.017"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5505"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157320"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/4038034"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.1838"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/5238028"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2805845"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.5555\/3015812.3015982"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.23"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219986"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.49"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0006353"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157527"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2007.08.020"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2015.05.016"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2010.5596796"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240566"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00478"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018303"},{"key":"e_1_3_1_26_2","volume-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","author":"Gao Junyu","year":"2020","unstructured":"Junyu Gao, Tianzhu Zhang, and Changsheng Xu. 2020. 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