{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:28:43Z","timestamp":1776083323701,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"King\u2019s College London, the China Scholarship Council and Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/M507222\/1"],"award-info":[{"award-number":["EP\/M507222\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by 67% more than rigid-attached sensors when the window size is 0.5s. Simulated and real human motion capture experiments with several participants confirm the model\u2019s predictions, demonstrating that this counterintuitive effect is accurately captured.<\/jats:p>","DOI":"10.3390\/s23104669","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T01:30:29Z","timestamp":1683855029000},"page":"4669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Probabilistic Model of Human Activity Recognition with Loose Clothing"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7807-1966","authenticated-orcid":false,"given":"Tianchen","family":"Shen","sequence":"first","affiliation":[{"name":"Centre for Robotics Research, Department of Engineeing, King\u2019s College London, London WC2R 2LS, UK"}]},{"given":"Irene","family":"Di Giulio","sequence":"additional","affiliation":[{"name":"Centre for Human and Applied Physiological Sciences, King\u2019s College London, London SE1 1UL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2534-4865","authenticated-orcid":false,"given":"Matthew","family":"Howard","sequence":"additional","affiliation":[{"name":"Centre for Robotics Research, Department of Engineeing, King\u2019s College London, London WC2R 2LS, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Anagnostis, A., Benos, L., Tsaopoulos, D., Tagarakis, A., Tsolakis, N., and Bochtis, D. 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