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A Data Glove-based KEM Dynamic Gesture Recognition Algorithm

Volume 14, Number 11, November 2018, pp. 2589-2600
DOI: 10.23940/ijpe.18.11.p5.25892600

Rui Hana,b, Zhiquan Fenga,b, Changsheng Aic, Wei Xied, and Kang Wanga,b

aSchool of Information Science and Engineering, University of Jinan, Jinan, 250022, China
bShandong Provincial Key Laboratory of Network based Intelligent Computing, Jinan, 250022, China
cSchool of Mechanical Engineering, University of Jinan, Jinan, 250022, China
dSchool of Information and Electrical Engineering, Harbin Institute of Technology at Weihai, WeiHai, 264209, China

(Submitted on August 16, 2018; Revised on September 14, 2018; Accepted on October 26, 2018)


Data gloves-based gesture recognition plays a very important role in the virtual reality interaction system. A new dynamic gesture recognition method, that is, K-means clustering dimensionality reduction and Euclidean metric template matching algorithm based on data glove (KEM algorithm), is proposed in this paper. First, high-dimensional data is clustered in the K-means clustering algorithm to achieve dimensionality reduction. Then, the low-dimensional data is put into the template matching method based on Euclidean metric to get the distance that matches all the templates. Finally, the corresponding gesture is identified according to the template matching. The main innovations of the proposed KEM algorithm are as follows: (a) K-means clustering is applied to dynamic gesture recognition for the first time to achieve real-time recognition, (b) the classical K-means method is optimized, and (c) the template matching process is more reasonable. Experiments show that the proposed KEM method can achieve 99.42% in recognition rate. The validity of the KEM method has been verified in a 3D Intelligent Teaching System.


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