Two gesture-computing approaches by using electromagnetic waves

Xianchao Wang, Yukun Guo, Jingzhi Li*, Hongyu LIU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

We are concerned with a novel sensor-based gesture input/ instruction technology which enables human beings to interact with computers conveniently. The human being wears an emitter on the finger or holds a digital pen that generates a time harmonic point charge. The inputs/instructions are performed through moving the finger or the digital pen. The computer recognizes the instruction by determining the motion trajectory of the dynamic point charge from the collected electromagnetic field measurement data. The identification process is mathematically modelled as a dynamic inverse source problem for time-dependent Maxwell’s equations. From a practical point of view, the point source should be assumed to move in an unknown inhomoge-neous background medium, which models the human body and the surroundings. Moreover, a salient feature is that the electromagnetic radiated data are only collected in a limited aperture. For the inverse problem, we develop, from the respectively deterministic and stochastic viewpoints, a dynamic direct sampling method and a modified particle filter method. Both approaches can effectively recover the motion trajectory. Rigorous theoretical justifications are presented for the mathematical modelling and the proposed recovery methods. Extensive numerical experiments are conducted to illustrate the promising features of the two proposed recognition approaches.

Original languageEnglish
Pages (from-to)879-901
Number of pages23
JournalInverse Problems and Imaging
Volume13
Issue number4
DOIs
Publication statusPublished - Aug 2019

Scopus Subject Areas

  • Analysis
  • Modelling and Simulation
  • Discrete Mathematics and Combinatorics
  • Control and Optimization

User-Defined Keywords

  • And phrases
  • Direct sampling
  • Dynamic inverse source problem
  • Electromagnetic wave
  • Gesture recognition
  • Particle filter

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