{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T19:54:43Z","timestamp":1725911683305},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T00:00:00Z","timestamp":1515369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,1,8]]},"abstract":"Automatic recognition of behavioral context (location, activities, body-posture etc.) can serve health monitoring, aging care, and many other domains. Recognizing context in-the-wild is challenging because of great variability in behavioral patterns, and it requires a complex mapping from sensor features to predicted labels. Data collected in-the-wild may be unbalanced and incomplete, with cases of missing labels or missing sensors. We propose using the multiple layer perceptron (MLP) as a multi-task model for context recognition. Based on features from multi-modal sensors, the model simultaneously predicts many diverse context labels. We analyze the advantages of the model's hidden layers, which are shared among all sensors and all labels, and provide insight to the behavioral patterns that these hidden layers may capture. We demonstrate how recognition of new labels can be improved when utilizing a model that was trained for an initial set of labels, and show how to train the model to withstand missing sensors. We evaluate context recognition on the previously published ExtraSensory Dataset, which was collected in-the-wild. Compared to previously suggested models, the MLP improves recognition, even with fewer parameters than a linear model. The ability to train a good model using data that has incomplete, unbalanced labeling and missing sensors encourages further research with uncontrolled, in-the-wild behavior.<\/jats:p>","DOI":"10.1145\/3161192","type":"journal-article","created":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T13:26:11Z","timestamp":1515504371000},"page":"1-22","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":74,"title":["Context Recognition In-the-Wild"],"prefix":"10.1145","volume":"1","author":[{"given":"Yonatan","family":"Vaizman","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering; University of California, San Diego, La Jolla, CA, USA"}]},{"given":"Nadir","family":"Weibel","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering; University of California, San Diego, La Jolla, CA, USA"}]},{"given":"Gert","family":"Lanckriet","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering; University of California, San Diego, La Jolla, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2018,1,8]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"2010 20th international conference on. IEEE, 3121--3124","author":"Brodersen Kay Henning","year":"2010","unstructured":"Kay Henning Brodersen , Cheng Soon Ong , Klaas Enno Stephan , and Joachim M Buhmann . 2010 . The balanced accuracy and its posterior distribution. In Pattern recognition (ICPR) , 2010 20th international conference on. IEEE, 3121--3124 . Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, and Joachim M Buhmann. 2010. The balanced accuracy and its posterior distribution. In Pattern recognition (ICPR), 2010 20th international conference on. IEEE, 3121--3124."},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2638728.2641673"},{"key":"e_1_2_2_3_1","first-page":"20","article-title":"Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Information Technology in Biomedicine","volume":"12","author":"Ermes Miikka","year":"2008","unstructured":"Miikka Ermes , Juha Parkka , Jani Mantyjarvi , and Ilkka Korhonen . 2008 . Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Information Technology in Biomedicine , IEEE Transactions on 12 , 1 (2008), 20 -- 26 . Miikka Ermes, Juha Parkka, Jani Mantyjarvi, and Ilkka Korhonen. 2008. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Information Technology in Biomedicine, IEEE Transactions on 12, 1 (2008), 20--26.","journal-title":"IEEE Transactions on"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s140305687"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1249\/MSS.0000000000000841"},{"key":"e_1_2_2_6_1","volume-title":"Asad Masood Khattak, and Teemu H Laine","author":"Khan Adil Mehmood","year":"2014","unstructured":"Adil Mehmood Khan , Ali Tufail , Asad Masood Khattak, and Teemu H Laine . 2014 . Activity recognition on smartphones via sensor-fusion and kda-based svms. International Journal of Distributed Sensor Networks 2014 (2014). Adil Mehmood Khan, Ali Tufail, Asad Masood Khattak, and Teemu H Laine. 2014. Activity recognition on smartphones via sensor-fusion and kda-based svms. International Journal of Distributed Sensor Networks 2014 (2014)."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-014-0810-3"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44851-9_15"},{"key":"e_1_2_2_10_1","volume-title":"Modeling missing data in clinical time series with RNNs. Machine Learning for Healthcare","author":"Lipton Zachary C","year":"2016","unstructured":"Zachary C Lipton , David C Kale , and Randall Wetzel . 2016. Modeling missing data in clinical time series with RNNs. Machine Learning for Healthcare ( 2016 ). Zachary C Lipton, David C Kale, and Randall Wetzel. 2016. Modeling missing data in clinical time series with RNNs. Machine Learning for Healthcare (2016)."},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472765"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMC.2001.973004"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971666"},{"key":"e_1_2_2_14_1","volume-title":"Proceedings of the 28th international conference on machine learning (ICML-11)","author":"Ngiam Jiquan","year":"2011","unstructured":"Jiquan Ngiam , Aditya Khosla , Mingyu Kim , Juhan Nam , Honglak Lee , and Andrew Y Ng . 2011 . Multimodal deep learning . In Proceedings of the 28th international conference on machine learning (ICML-11) . 689--696. Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. 2011. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11). 689--696."},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248010"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/11890348_39"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/PerComW.2013.6529487"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2014.05.007"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2015.7134104"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2017.3971131"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1889681.1889687"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNSC.2008.4525459"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3161192","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T20:08:13Z","timestamp":1672517293000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3161192"}},"subtitle":["Unified Model for Multi-Modal Sensors and Multi-Label Classification"],"short-title":[],"issued":{"date-parts":[[2018,1,8]]},"references-count":23,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,1,8]]}},"alternative-id":["10.1145\/3161192"],"URL":"http:\/\/dx.doi.org\/10.1145\/3161192","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,8]]},"assertion":[{"value":"2017-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-01-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}
