Noncontact Electromagnetic Wireless Recognition for Prosthesis Based on Intelligent Metasurface.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: May 7, 2022

Abstract

With the development of artificial intelligence and Internet of Things, hand gesture recognition techniques have attracted great attention owing to their excellent applications in developing human-machine interaction (HMI). Here, the authors propose a non-contact hand gesture recognition method based on intelligent metasurface. Owing to the advantage of dynamically controlling the electromagnetic (EM) focusing in the wavefront engineering, a transmissive programmable metasurface is presented to illuminate the forearm with more focusing spots and obtain comprehensive echo data, which can be processed under the machine learning technology to reach the non-contact gesture recognition with high accuracy. Compared with the traditional passive antennas, unique variations of echo coefficients resulted from near fields perturbed by finger and wrist agonist muscles can be aquired through the programmable metasurface by switching the positions of EM focusing. The authors realize the gesture recognition using support vector machine algorithm based on five individual focusing spots data and all-five-spot data. The influences of the focusing spots on the gesture recognition are analyzed through linear discriminant analysis algorithm and Fisher score. Experimental verifications prove that the proposed metasurface-based non-contact wireless design can realize the classification of hand gesture recognition with higher accuracy than traditional passive antennas, and give an HMI solution.

Authors

  • Hai Peng Wang
    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China.
  • Yu Xuan Zhou
    Department of Biomedical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China.
  • He Li
    National Soybean Processing Industry Technology Innovation Center, Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University Beijing 100048 China lihe@btbu.edu.cn liuxinqi@btbu.edu.cn.
  • Guo Dong Liu
    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China.
  • Si Meng Yin
    Department of Biomedical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China.
  • Peng Ju Li
    Department of Biomedical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China.
  • Shu Yue Dong
    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China.
  • Chao Yue Gong
    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China.
  • Shi Yu Wang
    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China.
  • Yun Bo Li
    State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China.
  • Tie Jun Cui
    State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China.

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