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Proceedings of the 2016 IEEE International Conference on IEEE Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia.","DOI":"10.1109\/PERCOMW.2016.7457154"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jcss.2011.04.004","article-title":"Optimal sampling from sliding windows","volume":"78","author":"Braverman","year":"2012","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_8","unstructured":"Wu, K.L., and Xia, Y. (2018). Adaptive Sampling Schemes for Clustering Streaming Graphs. (9,886,521), U.S. Patent."},{"key":"ref_9","unstructured":"Hentschel, B., Haas, P.J., and Tian, Y. (2018). Temporally-Biased Sampling for Online Model Management. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.inffus.2016.09.005","article-title":"Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges","volume":"35","author":"Gravina","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5109","DOI":"10.1080\/03610926.2015.1096388","article-title":"Effects of drift and noise on the optimal sliding window size for data stream regression models","volume":"46","author":"Tschumitschew","year":"2017","journal-title":"Commun. Stat.-Theory Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.pmcj.2012.07.003","article-title":"Activity recognition on streaming sensor data","volume":"10","author":"Krishnan","year":"2014","journal-title":"Pervasive Mob. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00779-010-0293-9","article-title":"Preprocessing techniques for context recognition from accelerometer data","volume":"14","author":"Figo","year":"2010","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Razzaq, M.A., Villalonga, C., Lee, S., Akhtar, U., Ali, M., Kim, E.S., Khattak, A.M., Seung, H., Hur, T., and Bang, J. (2017). mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification. Sensors, 17.","DOI":"10.3390\/s17102433"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window size impact in human activity recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5406","DOI":"10.3390\/s130505406","article-title":"A survey of body sensor networks","volume":"13","author":"Lai","year":"2013","journal-title":"Sensors"},{"key":"ref_17","unstructured":"(2018, September 11). UJAmI. Available online: http:\/\/ceatic.ujaen.es\/ujami\/sites\/default\/files\/2018-07\/UCAmI20Cup.zip."},{"key":"ref_18","unstructured":"(2018, September 11). UCAmI Cup 2018. Available online: http:\/\/mamilab.esi.uclm.es\/ucami2018\/UCAmICup.html."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/2499621","article-title":"A tutorial on human activity recognition using body-worn inertial sensors","volume":"46","author":"Bulling","year":"2014","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_20","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X., and Krishnaswamy, S. (2015, January 25\u201331). Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., and Cook, D.J. (2012, January 26\u201329). Simple and complex activity recognition through smart phones. Proceedings of the 2012 8th International Conference on Intelligent Environments (IE), Guanajuato, Mexico.","DOI":"10.1109\/IE.2012.39"},{"key":"ref_22","unstructured":"(2018, September 11). Python. Available online: https:\/\/www.python.org\/."},{"key":"ref_23","unstructured":"(2018, September 11). MySQL. Available online: https:\/\/www.mysql.com\/."},{"key":"ref_24","unstructured":"(2018, September 11). Weka. Available online: https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"171442","DOI":"10.1098\/rsos.171442","article-title":"Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour","volume":"5","author":"Walton","year":"2018","journal-title":"R. Soc. Open Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.schres.2006.03.021","article-title":"The five-factor model of the Positive and Negative Syndrome Scale II: A ten-fold cross-validation of a revised model","volume":"85","author":"Hoffman","year":"2006","journal-title":"Schizophr. R."}],"event":{"name":"The International Conference on Ubiquitous Computing and Ambient \u202aIntelligence\u202c\u202c","acronym":"UCAmI 2018"},"container-title":["UCAmI 2018"],"original-title":[],"link":[{"URL":"https:\/\/www.mdpi.com\/2504-3900\/2\/19\/1262\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:26:36Z","timestamp":1760196396000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-3900\/2\/19\/1262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,19]]},"references-count":26,"alternative-id":["proceedings2191262"],"URL":"https:\/\/doi.org\/10.3390\/proceedings2191262","relation":{},"subject":[],"published":{"date-parts":[[2018,10,19]]}}}