{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:21:40Z","timestamp":1781194900104,"version":"3.54.1"},"reference-count":67,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T00:00:00Z","timestamp":1547078400000},"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":"<jats:p>Dynamic hand gesture recognition has attracted increasing attention because of its importance for human\u2013computer interaction. In this paper, we propose a novel motion feature augmented network (MFA-Net) for dynamic hand gesture recognition from skeletal data. MFA-Net exploits motion features of finger and global movements to augment features of deep network for gesture recognition. To describe finger articulated movements, finger motion features are extracted from the hand skeleton sequence via a variational autoencoder. Global motion features are utilized to represent the global movements of hand skeleton. These motion features along with the skeleton sequence are then fed into three branches of a recurrent neural network (RNN), which augment the motion features for RNN and improve the classification performance. The proposed MFA-Net is evaluated on two challenging skeleton-based dynamic hand gesture datasets, including DHG-14\/28 dataset and SHREC\u201917 dataset. Experimental results demonstrate that our proposed method achieves comparable performance on DHG-14\/28 dataset and better performance on SHREC\u201917 dataset when compared with start-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s19020239","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:10:16Z","timestamp":1547179816000},"page":"239","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["MFA-Net: Motion Feature Augmented Network for Dynamic Hand Gesture Recognition from Skeletal Data"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2102-8235","authenticated-orcid":false,"given":"Xinghao","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2131-3044","authenticated-orcid":false,"given":"Guijin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengkai","family":"Guo","sequence":"additional","affiliation":[{"name":"AI Lab, Bytedance Inc., Beijing 100086, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cairong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Huajie IMI Technology Co., Ltd, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TSMCC.2007.893280","article-title":"Gesture recognition: A survey","volume":"37","author":"Mitra","year":"2007","journal-title":"IEEE Trans. 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