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The proposed approach is highly versatile, capable of detecting a wide range of anomalies associated with daily living activities. We focus on activities that exhibit abnormal duration, frequency, or entirely new behaviors that deviate from established routines. The performance evaluation of our framework revolves around two key aspects: reliability and adaptability. Reliability measures the accuracy of detecting unusual events, while adaptability assesses the system\u2019s ability to accommodate changes in user behavior. This involves recognizing recurrent anomalous behaviors as new norms over time and transitioning from persistent anomalies during an initial phase. Our proposed anomaly detection system demonstrates promising results in real-life scenarios. It achieves good reliability, with true negative rate and true positive rate exceeding 90% and 80% respectively, across all activities and users. Additionally, the system swiftly adapts to new individuals or their evolving behaviors, adjusting within a span of 3 to 7 days for new behaviors.<\/jats:p>","DOI":"10.3233\/ais-230436","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T11:45:30Z","timestamp":1718970330000},"page":"365-387","source":"Crossref","is-referenced-by-count":0,"title":["An unsupervised anomaly detection framework for smart assisted living via growing neural gas networks"],"prefix":"10.1177","volume":"16","author":[{"given":"Matteo","family":"Ciprian","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, Via Gradenigo 6\/B, 35131, Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Gadaleta","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, Via Gradenigo 6\/B, 35131, Padova, Italy"},{"name":"Scripps Research Translational 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