{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:18:00Z","timestamp":1778717880935,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"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>As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial\u2013temporal graph convolutional networks\u2019 bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.<\/jats:p>","DOI":"10.3390\/s23167274","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T01:49:34Z","timestamp":1692582574000},"page":"7274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction"],"prefix":"10.3390","volume":"23","author":[{"given":"Kun","family":"Han","sequence":"first","affiliation":[{"name":"School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shuping, N., and Feng, W. 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