Int J Performability Eng ›› 2018, Vol. 14 ›› Issue (11): 2590-2600.doi: 10.23940/ijpe.18.11.p5.25892600

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A Data Glove-based KEM Dynamic Gesture Recognition Algorithm

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

  1. a School of Information Science and Engineering, University of Jinan, Jinan, 250022, China;
    b Shandong Provincial Key Laboratory of Network based Intelligent Computing, Jinan, 250022, China;
    c School of Mechanical Engineering, University of Jinan, Jinan, 250022, China;
    d School of Information and Electrical Engineering, Harbin Institute of Technology at Weihai, WeiHai, 264209, China
  • Submitted on ;
  • Contact: * E-mail address: ise_fengzq@ujn.edu.cn
  • About author:Rui Han is a Master's student in the School of Information Science and Engineering at the University of Jinan. The areas of interest for her research cover human-computer interactions and virtual reality.Zhiquan Feng received his Ph.D. from the School of Computer Science and Technology at Shandong University. He is currently a professor in the School of Information Science and Engineering at the University of Jinan. He is the deputy director of the Shandong Provincial Key Laboratory of Network Based Intelligent Computing; a member of the editorial board of Computer Aided Drafting Design and Manufacturing and The Open Virtual Reality Journal; a reviewer of the Journal of Computer Applications, Journal of Image and Graphics, and Journal of Computer-Aided Design & Computer Graphics; a deputy editor of the World Research Journal of Pattern Recognition; and a visiting professor of Sichuan Mianyang Normal University. The areas of interest for his research cover human-computer interactions, virtual reality, and image processing.Changsheng Ai received his Ph.D. from the School of Precision Instruments and Optoelectronics at Tianjin University. He is currently a professor in the School of Mechanical Engineering at the University of Jinan. The areas of interest for his research cover embedded measurement and control system development and mechatronics technology applications.Wei Xie is an associate professor in the School of Information and Electrical Engineering at Harbin Institute of Technology at Weihai.Kang Wang is a Master's student in the School of Information Science and Engineering at the University of Jinan. The areas of interest for his research cover human-computer interactions and deep learning.

Abstract: 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.

Key words: dynamic gesture recognition, data gloves, K-means, Euclidean metric, template matching, 3D recognition and interaction