{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:30:23Z","timestamp":1778257823879,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,12]],"date-time":"2020-07-12T00:00:00Z","timestamp":1594512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006537","name":"Campus France","doi-asserted-by":"publisher","award":["44764WK"],"award-info":[{"award-number":["44764WK"]}],"id":[{"id":"10.13039\/501100006537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000308","name":"British Council","doi-asserted-by":"publisher","award":["515095884"],"award-info":[{"award-number":["515095884"]}],"id":[{"id":"10.13039\/501100000308","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people.<\/jats:p>","DOI":"10.3390\/rs12142237","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T09:30:49Z","timestamp":1594719049000},"page":"2237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Hierarchical Radar Data Analysis for Activity and Personnel Recognition"],"prefix":"10.3390","volume":"12","author":[{"given":"Xingzhuo","family":"Li","sequence":"first","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"},{"name":"Glasgow College \u2013 UESTC, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenghui","family":"Li","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Fioranelli","sequence":"additional","affiliation":[{"name":"MS3-Microwave Sensing Signals and Systems, TU Delft, 2600-2629 Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shufan","family":"Yang","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Romain","sequence":"additional","affiliation":[{"name":"ETIS \u2013 Signal and Information Processing lab, University Cergy-Pontoise, 95000 Cergy, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2124-6803","authenticated-orcid":false,"given":"Julien Le","family":"Kernec","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK"},{"name":"ETIS \u2013 Signal and Information Processing lab, University Cergy-Pontoise, 95000 Cergy, France"},{"name":"School of Information and Communication, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chavan, M., Pardeshi, P., Khoje, S.A., and Patil, M. 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