{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:31:14Z","timestamp":1763202674183,"version":"3.41.0"},"reference-count":15,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2022,10,7]]},"abstract":"<jats:p>Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for a wide range of sensing applications. Most existing works require substantial amounts of wellcurated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. This article presents a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. With the representations learned via our model, task-specific models trained with limited labeled samples can achieve superior performances in typical IMU sensing applications, such as Human Activity Recognition (HAR).<\/jats:p>","DOI":"10.1145\/3568113.3568124","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T04:06:47Z","timestamp":1665547607000},"page":"39-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["LIMU-BERT"],"prefix":"10.1145","volume":"26","author":[{"given":"Huatao","family":"Xu","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"given":"Pengfei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"given":"Mo","family":"Li","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"given":"Guobin","family":"Shen","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328932"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052577"},{"volume-title":"Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services.","author":"Yang Liu","key":"e_1_2_1_3_1","unstructured":"Liu Yang , Real-time arm skeleton tracking and gesture inference tolerant to missing wearable sensors. 2019 . Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. Liu Yang, et al. Real-time arm skeleton tracking and gesture inference tolerant to missing wearable sensors. 2019. Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2837758"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806333"},{"volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint. arXiv:1810.04805.","year":"2018","key":"e_1_2_1_6_1","unstructured":"Devlin, Jacob, 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint. arXiv:1810.04805. Devlin, Jacob, et al. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint. arXiv:1810.04805."},{"key":"e_1_2_1_7_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E. Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba , Jamie Ryan Kiros, and Geoffrey E. Hinton . 2016 . Layer normalization. arXiv preprint, arXiv:1607.06450. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. arXiv preprint, arXiv:1607.06450."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00300"},{"key":"e_1_2_1_9_1","volume-title":"Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942.","author":"Lan Zhenzhong","year":"2019","unstructured":"Zhenzhong Lan , 2019 . Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942. Zhenzhong Lan, et al. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942."},{"key":"e_1_2_1_10_1","unstructured":"Ashish Vaswani etal 2017. Attention is all you need. Advances in Neural Information Processing Systems 30.  Ashish Vaswani et al. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30."},{"volume-title":"Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems.","author":"Stisen Allan","key":"e_1_2_1_11_1","unstructured":"Allan Stisen , Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition. 2015 . Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. Allan Stisen, et al. Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition. 2015. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.07.085"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302505.3310068"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/s140610146"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3380985"}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3568113.3568124","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3568113.3568124","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:26:15Z","timestamp":1750281975000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3568113.3568124"}},"subtitle":["Unleashing the Potential of Unlabeled Data for IMU Sensing Applications"],"short-title":[],"issued":{"date-parts":[[2022,10,7]]},"references-count":15,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,10,7]]}},"alternative-id":["10.1145\/3568113.3568124"],"URL":"https:\/\/doi.org\/10.1145\/3568113.3568124","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"type":"print","value":"2375-0529"},{"type":"electronic","value":"2375-0537"}],"subject":[],"published":{"date-parts":[[2022,10,7]]},"assertion":[{"value":"2022-10-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}