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In addition, we propose a consistent filter that utilizes two heterogeneous classifiers to automatically select high-confidence instances from the target domain to jointly enhance the performance on the target task. The effectiveness and performance of our model are evaluated through comprehensive experiments on two activity recognition benchmarks and a private activity recognition data set (collected by our signal sensors), where our model outperforms traditional transfer learning methods at HAR.<\/jats:p>","DOI":"10.1007\/s40747-023-01218-w","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T04:01:25Z","timestamp":1694577685000},"page":"1459-1471","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A holistic multi-source transfer learning approach using wearable sensors for personalized daily activity recognition"],"prefix":"10.1007","volume":"10","author":[{"given":"Qi","family":"Jia","sequence":"first","affiliation":[]},{"given":"Jing","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Po","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9893-3436","authenticated-orcid":false,"given":"Yun","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"issue":"12","key":"1218_CR1","doi-asserted-by":"publisher","first-page":"5859","DOI":"10.1109\/TCYB.2019.2960481","volume":"51","author":"MM Arzani","year":"2021","unstructured":"Arzani MM, Fathy M, Azirani AA, Adeli E (2021) Switching structured prediction for simple and complex human activity recognition. 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