{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:44:47Z","timestamp":1760229887865,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UEFISCDI Romania and the European Union AAL Programm with co-funding from the Horizon 2020 research and innovation program","award":["AAL159\/2020 H2HCare","AAL264\/2021 engAGE","AAL162\/2020 ReMember-Me"],"award-info":[{"award-number":["AAL159\/2020 H2HCare","AAL264\/2021 engAGE","AAL162\/2020 ReMember-Me"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person\u2019s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.<\/jats:p>","DOI":"10.3390\/s22134803","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"4803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns"],"prefix":"10.3390","volume":"22","author":[{"given":"Viorica Rozina","family":"Chifu","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3194-080X","authenticated-orcid":false,"given":"Cristina Bianca","family":"Pop","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandru Miron","family":"Rancea","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrei","family":"Morar","sequence":"additional","affiliation":[{"name":"Montran SRL, Alexandru Vaida Voevod 16, 400592 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tudor","family":"Cioara","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2998-4176","authenticated-orcid":false,"given":"Marcel","family":"Antal","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6166-5266","authenticated-orcid":false,"given":"Ionut","family":"Anghel","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,25]]},"reference":[{"key":"ref_1","unstructured":"(2022, April 15). 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