{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:49:27Z","timestamp":1757544567527,"version":"3.41.2"},"reference-count":26,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p> Static Chinese Sign Language Recognition (SCSLR) is an important field of research in human\u2013computer interaction and assistive technology. Traditional SCSLR methods usually rely on computer vison sensors, which are susceptible to effects such as hand shapes, lighting conditions, and occlusions, resulting in low recognition accuracy. Additionally, sensor-based SCSLR methods cannot achieve high recognition accuracy due to limited hand gesture information. In this paper, we propose a multi-sensor fusion method, using a DE\u2013XGBoost model, to fuse the information of hand gesture and finger curvature to achieve the SCSLR, which can overcome the recognition error problems caused by insufficient sign language information. In addition, we design and implement a prototype system, which consists of a smartphone and a smart glove, to evaluate our proposed method in comparison with support vector machine (SVM), XGBoost, gcForest, and artificial neural network (ANN). Experimental results show that our proposed method achieves a better performance in terms of accuracy, robustness, and real-time processing. <\/jats:p>","DOI":"10.1142\/s1793962325410016","type":"journal-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T01:36:50Z","timestamp":1694655410000},"source":"Crossref","is-referenced-by-count":2,"title":["A multi-sensor fusion method for static Chinese sign language recognition using DE\u2013XGBoost"],"prefix":"10.1142","volume":"16","author":[{"given":"Xiaoyang","family":"Lu","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney Ultimo, Sydney, NSW 2007, Australia"}]},{"given":"Yanjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Hydropower and Water Conservancy, Engineering Huadong Engineering Corporation Limited, Hangzhou, Zhejiang 311122, P. R. 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