{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:10:36Z","timestamp":1761581436201,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2016,6,15]],"date-time":"2016-06-15T00:00:00Z","timestamp":1465948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science &amp; Technology Pillar Program","award":["2014BAK12B02"],"award-info":[{"award-number":["2014BAK12B02"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61327805"],"award-info":[{"award-number":["61327805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method.<\/jats:p>","DOI":"10.3390\/s16060885","type":"journal-article","created":{"date-parts":[[2016,6,15]],"date-time":"2016-06-15T14:11:19Z","timestamp":1465999879000},"page":"885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Detection and Classification of Finer-Grained Human Activities Based on Stepped-Frequency Continuous-Wave Through-Wall Radar"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-8645","authenticated-orcid":false,"given":"Fugui","family":"Qi","sequence":"first","affiliation":[{"name":"Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi\u2019an 710032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fulai","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi\u2019an 710032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi\u2019an 710032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuantao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi\u2019an 710032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuming","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi\u2019an 710032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi\u2019an 710032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fairchild, D.P., and Narayanan, R.M. 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