{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T04:59:17Z","timestamp":1768280357516,"version":"3.49.0"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"content-version":"vor","delay-in-days":302,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011324","name":"Qingdao University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011324","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region\u2010Convolutional Neural Network (MR\u2010CNN)\u2010 based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR\u2010CNN model is based on the framework and structure of the region\u2010convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR\u2010CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth\u2010separable convolution to improve the network efficiency. Experimentally constructed polygon\u2010labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth\u2010separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR\u2010CNN.<\/jats:p>","DOI":"10.1155\/2021\/4334024","type":"journal-article","created":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T03:05:12Z","timestamp":1635649512000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Neural Network\u2010Oriented Big Data Model for Yoga Movement Recognition"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5549-8289","authenticated-orcid":false,"given":"Hui","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_2_9_1_2","article-title":"Effect of tai chi on markers of oxidative stress: systematic review and meta-analysis","volume":"18","author":"Juana R. 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