{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:33:36Z","timestamp":1773153216349,"version":"3.50.1"},"reference-count":169,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,9]],"date-time":"2020-05-09T00:00:00Z","timestamp":1588982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.<\/jats:p>","DOI":"10.3390\/s20092702","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T12:26:30Z","timestamp":1589199990000},"page":"2702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Unsupervised Human Activity Recognition Using the Clustering Approach: A Review"],"prefix":"10.3390","volume":"20","author":[{"given":"Paola","family":"Ariza Colpas","sequence":"first","affiliation":[{"name":"Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia"}]},{"given":"Enrico","family":"Vicario","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4926-7414","authenticated-orcid":false,"given":"Emiro","family":"De-La-Hoz-Franco","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia"}]},{"given":"Marlon","family":"Pineres-Melo","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Universidad del Norte, Barranquilla 081001, Colombia"}]},{"given":"Ana","family":"Oviedo-Carrascal","sequence":"additional","affiliation":[{"name":"Faculty of Engineering in Information and Communication Technologies, Universidad Pontificia Bolivariana, Medell\u00edn 050031, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9050-088X","authenticated-orcid":false,"given":"Fulvio","family":"Patara","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/34.824819","article-title":"Statistical pattern recognition: A review","volume":"22","author":"Jain","year":"2000","journal-title":"IEEE Trans. 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