{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:58:27Z","timestamp":1760241507863,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,14]],"date-time":"2018-04-14T00:00:00Z","timestamp":1523664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union's Horizon 2020 research and innovation programme (Marie Sklodowska-Curie)","award":["734355"],"award-info":[{"award-number":["734355"]}]},{"name":"Spanish government","award":["TIN2015-66524-P"],"award-info":[{"award-number":["TIN2015-66524-P"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities.<\/jats:p>","DOI":"10.3390\/s18041202","type":"journal-article","created":{"date-parts":[[2018,4,16]],"date-time":"2018-04-16T03:12:09Z","timestamp":1523848329000},"page":"1202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Using Ontologies for the Online Recognition of Activities of Daily Living"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9221-7351","authenticated-orcid":false,"given":"Alberto","family":"Salguero","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of C\u00e1diz, C\u00e1diz 11519, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1118-7782","authenticated-orcid":false,"given":"Macarena","family":"Espinilla","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Ja\u00e9n, Ja\u00e9n 23071, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5685-9250","authenticated-orcid":false,"given":"Pablo","family":"Delatorre","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of C\u00e1diz, C\u00e1diz 11519, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8577-8772","authenticated-orcid":false,"given":"Javier","family":"Medina","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Ja\u00e9n, Ja\u00e9n 23071, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TSMCC.2012.2198883","article-title":"Sensor-based activity recognition","volume":"42","author":"Chen","year":"2012","journal-title":"IEEE Trans. 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