{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T09:12:34Z","timestamp":1773393154353,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R511705\/1 and EP\/T021063\/1"],"award-info":[{"award-number":["EP\/R511705\/1 and EP\/T021063\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ajman University Internal Research Grant, and in part by Taif University, Taif, Saudi Arabia, through the Taif University Research Grant","award":["TURSP-2020\/277"],"award-info":[{"award-number":["TURSP-2020\/277"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal\u2019s Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking.<\/jats:p>","DOI":"10.3390\/s22030809","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:34:40Z","timestamp":1642970080000},"page":"809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multiple Participants\u2019 Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7666-838X","authenticated-orcid":false,"given":"Umer","family":"Saeed","sequence":"first","affiliation":[{"name":"Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2799-1791","authenticated-orcid":false,"given":"Syed","family":"Yaseen Shah","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2052-1121","authenticated-orcid":false,"given":"Syed","family":"Aziz Shah","sequence":"additional","affiliation":[{"name":"Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4212-2503","authenticated-orcid":false,"given":"Haipeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6463-7903","authenticated-orcid":false,"given":"Abdullah","family":"Alhumaidi Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, College of Ranyah, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6674-7890","authenticated-orcid":false,"given":"Turke","family":"Althobaiti","sequence":"additional","affiliation":[{"name":"Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5088-1462","authenticated-orcid":false,"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3950-4719","authenticated-orcid":false,"given":"Sana","family":"Ullah Jan","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-8248","authenticated-orcid":false,"given":"Jawad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7097-9969","authenticated-orcid":false,"given":"Qammer H.","family":"Abbasi","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MAES.2019.2933971","article-title":"RF sensing technologies for assisted daily living in healthcare: A comprehensive review","volume":"34","author":"Shah","year":"2019","journal-title":"IEEE Aerosp. 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