{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:10:00Z","timestamp":1774541400861,"version":"3.50.1"},"reference-count":150,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Higher Education Commission, Pakistan","award":["No.5-1\/HRD\/UESTPI(Batch-VI)\/7108\/2018\/HEC"],"award-info":[{"award-number":["No.5-1\/HRD\/UESTPI(Batch-VI)\/7108\/2018\/HEC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The increase in the number of action recognition datasets intersects with advances in deep learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality\u2014such as RGB, depth, skeleton, and infrared (IR)\u2014has distinct characteristics; therefore, it is important to exploit the value of each modality for better action recognition. In this paper, we focus solely on data fusion and recognition techniques in the context of vision with an RGB-D perspective. We conclude by discussing research challenges, emerging trends, and possible future research directions.<\/jats:p>","DOI":"10.3390\/s21124246","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T13:29:58Z","timestamp":1624282198000},"page":"4246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["RGB-D Data-Based Action Recognition: A Review"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9042-5018","authenticated-orcid":false,"given":"Muhammad Bilal","family":"Shaikh","sequence":"first","affiliation":[{"name":"School of Engineering, Edith Cowan University, Perth, WA 6027, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9004-7608","authenticated-orcid":false,"given":"Douglas","family":"Chai","sequence":"additional","affiliation":[{"name":"School of Engineering, Edith Cowan University, Perth, WA 6027, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.1109\/JSEN.2015.2416651","article-title":"Evaluating and Improving the Depth Accuracy of Kinect for Windows v2","volume":"15","author":"Yang","year":"2015","journal-title":"IEEE Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Carfagni, M., Furferi, R., Governi, L., Santarelli, C., Servi, M., Uccheddu, F., and Volpe, Y. 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