{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T17:24:18Z","timestamp":1761845058533,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T00:00:00Z","timestamp":1565740800000},"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>In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers\u2019 daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised\/semi-supervised ones in terms of detection accuracy and lead-time of prediction.<\/jats:p>","DOI":"10.3390\/s19163549","type":"journal-article","created":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T04:22:54Z","timestamp":1565842974000},"page":"3549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["AI-Based Early Change Detection in Smart Living Environments"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9737-3721","authenticated-orcid":false,"given":"Giovanni","family":"Diraco","sequence":"first","affiliation":[{"name":"CNR\u2014National Research Council of Italy, IMM\u2014Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8970-3313","authenticated-orcid":false,"given":"Alessandro","family":"Leone","sequence":"additional","affiliation":[{"name":"CNR\u2014National Research Council of Italy, IMM\u2014Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Siciliano","sequence":"additional","affiliation":[{"name":"CNR\u2014National Research Council of Italy, IMM\u2014Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1089\/tmj.2013.0109","article-title":"Monitoring activities of daily living of the elderly and the potential for its use in telecare and telehealth: A review","volume":"19","author":"Gokalp","year":"2013","journal-title":"Telemed. 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