{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:05:13Z","timestamp":1781535913360,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"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>Human activity recognition (HAR) algorithms today are designed and evaluated on data collected in controlled settings, providing limited insights into their performance in real-world situations with noisy and missing sensor data and natural human activities. We present a real-world HAR open dataset compiled from a wristband equipped with a triaxial accelerometer. During data collection, participants had autonomy in their daily life activities, and the process remained unobserved and uncontrolled. A general convolutional neural network model was trained on this dataset, achieving a mean balanced accuracy (MBA) of 80%. Personalizing the general model through transfer learning can yield comparable and even superior results using fewer data, with the MBA improving to 85%. To emphasize the issue of insufficient real-world training data, we conducted training of the model using the public MHEALTH dataset, resulting in 100% MBA. However, upon evaluating the MHEALTH-trained model on our real-world dataset, the MBA drops to 62%. After personalizing the model with real-world data, an improvement of 17% in the MBA is achieved. This paper showcases the potential of transfer learning to make HAR models trained in different contexts (lab vs. real-world) and on different participants perform well for new individuals with limited real-world labeled data available.<\/jats:p>","DOI":"10.3390\/s23104606","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T01:57:51Z","timestamp":1683683871000},"page":"4606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9692-9874","authenticated-orcid":false,"given":"Marija","family":"Stojchevska","sequence":"first","affiliation":[{"name":"IDLab, Ghent University-imec, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"De Brouwer","sequence":"additional","affiliation":[{"name":"IDLab, Ghent University-imec, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9971-3128","authenticated-orcid":false,"given":"Martijn","family":"Courteaux","sequence":"additional","affiliation":[{"name":"IDLab, Ghent University-imec, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2529-5477","authenticated-orcid":false,"given":"Femke","family":"Ongenae","sequence":"additional","affiliation":[{"name":"IDLab, Ghent University-imec, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7865-6793","authenticated-orcid":false,"given":"Sofie","family":"Van Hoecke","sequence":"additional","affiliation":[{"name":"IDLab, Ghent University-imec, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107561","DOI":"10.1016\/j.patcog.2020.107561","article-title":"Sensor-based and vision-based human activity recognition: A comprehensive survey","volume":"108","author":"Dang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bouchabou, D., Nguyen, S.M., Lohr, C., LeDuc, B., and Kanellos, I. 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