{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:30:53Z","timestamp":1775579453661,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education (MOE), Malaysia, under the Fundamental Research Grant Scheme","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/2"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.<\/jats:p>","DOI":"10.3390\/s22218458","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T04:00:51Z","timestamp":1667534451000},"page":"8458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Incremental Learning of Human Activities in Smart Homes"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3081-2950","authenticated-orcid":false,"given":"Sook-Ling","family":"Chua","sequence":"first","affiliation":[{"name":"Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lee Kien","family":"Foo","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hans W.","family":"Guesgen","sequence":"additional","affiliation":[{"name":"School of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9568-848X","authenticated-orcid":false,"given":"Stephen","family":"Marsland","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2020). 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