{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T16:22:11Z","timestamp":1723566131512},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:00:00Z","timestamp":1667606400000},"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":"We propose in this work a dynamic group sparsity (DGS) based time-frequency feature extraction method for dynamic hand gesture recognition (HGR) using millimeter-wave radar sensors. Micro-Doppler signatures of hand gestures show both sparse and structured characteristics in time-frequency domain, but previous study only focus on sparsity. We firstly introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures. The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features. The experiment shows that the proposed method achieved 3.3% recognition accuracy improvement over the sparsity-based method and has a better recognition accuracy than CNN based method in small dataset.<\/jats:p>","DOI":"10.3390\/s22218535","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T08:02:22Z","timestamp":1667808142000},"page":"8535","source":"Crossref","is-referenced-by-count":4,"title":["Clustering-Driven DGS-Based Micro-Doppler Feature Extraction for Automatic Dynamic Hand Gesture Recognition"],"prefix":"10.3390","volume":"22","author":[{"given":"Chengjin","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Zehao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Qiang","family":"An","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Fourth Military Medical University, Xi\u2019an 710032, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2990-0497","authenticated-orcid":false,"given":"Shiyong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Ahmad","family":"Hoorfar","sequence":"additional","affiliation":[{"name":"Antenna Research Laboratory, Center for Advanced Communications, Villanova University, Villanova, PA 19085, USA"}]},{"given":"Chenxiao","family":"Kou","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,5]]},"reference":[{"key":"ref_1","unstructured":"Wu, Q., and Zhao, D. 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