{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:19:46Z","timestamp":1768814386669,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"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>With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone.<\/jats:p>","DOI":"10.3390\/s25072299","type":"journal-article","created":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T05:30:59Z","timestamp":1744003859000},"page":"2299","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4585-3907","authenticated-orcid":false,"given":"Jiaxuan","family":"Wu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, No. 6, Nanping Central Road, Hunnan New District, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, No. 6, Nanping Central Road, Hunnan New District, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueqiu","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, No. 6, Nanping Central Road, Hunnan New District, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"ref_1","unstructured":"Zhao, L.X. 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