{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:23:22Z","timestamp":1780053802901,"version":"3.54.0"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Brain Korea 21 FOUR, the Ministry of Science and ICT (MSIT), South Korea, under the ITRC Support Program Supervised","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute for Information Communication Technology Planning and Evaluation","award":["IITP-2020-0-01749"],"award-info":[{"award-number":["IITP-2020-0-01749"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea funded by the Korean Government","doi-asserted-by":"publisher","award":["RS-2022-00144190"],"award-info":[{"award-number":["RS-2022-00144190"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3221425","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T20:31:53Z","timestamp":1668112313000},"page":"119333-119344","source":"Crossref","is-referenced-by-count":10,"title":["Sensor-Based Open-Set Human Activity Recognition Using Representation Learning With Mixup Triplets"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7110-7353","authenticated-orcid":false,"given":"Minjung","family":"Lee","sequence":"first","affiliation":[{"name":"School of Industrial and Management Engineering, Korea University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2205-8516","authenticated-orcid":false,"given":"Seoung Bum","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Industrial and Management Engineering, Korea University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref38","volume":"5","author":"walpole","year":"1993","journal-title":"Probability and Statistics for Engineers and Scientists"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM50583.2021.9439116"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5220\/0007916806560663"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.202"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75768-7_28"},{"key":"ref37","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01065"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-019-01445-x"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7439(99)00047-7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2991\/icaita-16.2016.13"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/431"},{"key":"ref12","article-title":"Human activity recognition from wearable sensor data using self-attention","author":"mahmud","year":"2020","journal-title":"arXiv 2003 09018"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SMC42975.2020.9283381"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/s16010115","article-title":"Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition","volume":"16","author":"ord\u00f3\u00f1ez","year":"2016","journal-title":"SENSORS"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2982225"},{"key":"ref16","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2018.8486601"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-63649-6"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00361"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1192\/1\/012017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3130761"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1186\/s40798-020-0237-5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1254"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.030"},{"key":"ref5","first-page":"1","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"chen","year":"2021","journal-title":"ACM Comput Surv"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/s16010115","article-title":"Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition","volume":"16","author":"ord\u00f3\u00f1ez","year":"2016","journal-title":"SENSORS"},{"key":"ref7","first-page":"3995","article-title":"Deep convolutional neural networks on multichannel time series for human activity recognition","author":"yang","year":"2015","journal-title":"Proc 24th Int Joint Conf Artif Intell"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s21051636"},{"key":"ref9","first-page":"915","article-title":"Real-time human activity recognition from accelerometer data using convolutional neural networks","volume":"62","author":"andrey","year":"2017","journal-title":"Appl Soft Comput"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2985082"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3130761"},{"key":"ref22","article-title":"Mixup: Beyond empirical risk minimization","author":"zhang","year":"2017","journal-title":"arXiv 1710 09412"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370438"},{"key":"ref23","first-page":"437","article-title":"A public domain dataset for human activity recognition using smartphones","author":"anguita","year":"2013","journal-title":"Proc 21st Int Eur Symp Artif Neural Netw Comput Intell Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00366"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09945967.pdf?arnumber=9945967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T20:01:20Z","timestamp":1670875280000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9945967\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3221425","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}