{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:56:44Z","timestamp":1760057804084,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61976124","62173053"],"award-info":[{"award-number":["61976124","62173053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>For daily activity recognition in smart homes, it is possible to reduce the effort required for labeling by transferring a trained model. This involves utilizing a labeled daily activity dataset from one smart home to recognize other activities in another. The foundation of this transfer lies in establishing a shared common feature space between the two smart homes, achieved through a feature mapping approach for daily activities. However, existing heuristic feature mapping methods are often coarse, resulting in only moderate recognition performance. In this paper, we propose a fine-grained daily activity feature mapping approach. Sensors are ranked by their significance using the PageRank algorithm, and a novel alignment algorithm is introduced for sensor mapping. Experiments conducted on the publicly available CASAS dataset demonstrate that the proposed method significantly outperforms existing daily activity feature mapping approaches.<\/jats:p>","DOI":"10.3390\/a18030131","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T11:22:12Z","timestamp":1740568932000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-Grained Mapping Between Daily Activity Features in Smart Homes"],"prefix":"10.3390","volume":"18","author":[{"given":"Yahui","family":"Wang","sequence":"first","affiliation":[{"name":"Electronic Information Department, Jinzhong Vocational and Technical College, Jinzhong 030601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Electronic Information Department, Jinzhong Vocational and Technical College, Jinzhong 030601, China"},{"name":"School of Information Science & Technology, Dalian Maritime University, Dalian 116026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhalotia, N., Kumar, M., Alameen, A., Mohapatra, H., and Kolhar, M. 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