{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T11:09:57Z","timestamp":1787137797414,"version":"build-2736575974"},"reference-count":62,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"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>Background and Objective: The intricate connection between daily behaviours and health necessitates robust monitoring, particularly with the advent of Internet of Things (IoT) systems. This study introduces an innovative approach that exploits the synergy of information from various IoT sources to assess the alignment of behavioural routines with health guidelines. The goal is to improve the readability of behaviour models and provide actionable insights for healthcare professionals. Method: We integrate data from ambient sensors, smartphones, and wearable devices to acquire daily behavioural routines by employing process mining (PM) techniques to generate interpretable behaviour models. These routines are grouped according to compliance with health guidelines, and a clustering method is used to identify similarities in behaviours and key characteristics within each cluster. Results: Applied to an elderly care case study, our approach categorised days into three physical activity levels (Insufficient, Sufficient, Desirable) based on daily step thresholds. The integration of multi-source data revealed behavioural variations not detectable through single-source monitoring. We demonstrated that the proposed visualisations in calendar and timeline views aid health experts in understanding patient behaviours, enabling longitudinal monitoring and clearer interpretation of behavioural trends and precise interventions. Notably, the approach facilitates early detection of behaviour changes during contextual events (e.g., COVID-19 lockdown and Ramadan), which are available in our dataset. Conclusions: By enhancing interpretability and linking behaviour to health guidelines, this work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous monitoring with IoT-driven data analysis.<\/jats:p>","DOI":"10.3390\/s25185619","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T19:16:17Z","timestamp":1757445377000},"page":"5619","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Synergy of Information in Multimodal Internet of Things Systems\u2014Discovering the Impact of Daily Behaviour Routines on Physical Activity Level"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0584-4360","authenticated-orcid":false,"given":"Mohsen","family":"Shirali","sequence":"first","affiliation":[{"name":"Department of Computing Science and Engineering (INGI), Universit\u00e9 Catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2313-5392","authenticated-orcid":false,"given":"Zahra","family":"Ahmadi","sequence":"additional","affiliation":[{"name":"Research Centre for Information Systems Engineering (LIRIS), KU Leuven, 1000 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2602-6367","authenticated-orcid":false,"given":"Jose Luis","family":"Bayo-Monton","sequence":"additional","affiliation":[{"name":"Process Mining 4 Health Lab\u2013SABIEN-ITACA Institute, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4903-3896","authenticated-orcid":false,"given":"Zoe","family":"Valero-Ramon","sequence":"additional","affiliation":[{"name":"Process Mining 4 Health Lab\u2013SABIEN-ITACA Institute, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2819-5597","authenticated-orcid":false,"given":"Carlos","family":"Fernandez-Llatas","sequence":"additional","affiliation":[{"name":"Process Mining 4 Health Lab\u2013SABIEN-ITACA Institute, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"},{"name":"Department of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet, 17177 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TITB.2009.2037317","article-title":"SVM-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results","volume":"14","author":"Fleury","year":"2009","journal-title":"IEEE Trans. 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