2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPCOM-7.3
Paper Title RECOGNITION OF DYNAMIC HAND GESTURE BASED ON MM-WAVE FMCW RADAR MICRO-DOPPLER SIGNATURES
Authors Wen Jiang, Yihui Ren, Ying Liu, University of Chinese Academy of Sciences, China; Ziao Wang, Xinghua Wang, Beijing Institute of Technology, China
SessionSPCOM-7: Communication-enabled Applications
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Signal Processing for Communications and Networking: [SPC-HIGH] High frequency and wideband communication
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Radar-based sensors provide an attractive choice for hand gesture recognition (HGR). The very challenging problems in radar-based HGR are radar echo data preprocessing and recognition accuracy. In this paper, we propose a convolutional neural network (CNN) for dynamic HGR based on a millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar which operates at 77GHz. Six different dynamic hand gestures are designed and the time-frequency analysis of micro-Doppler signatures are adopted as the input to CNN. The measured data of the dynamic hand gestures are collected in different experimental scenarios. The recognition accuracy of the six gestures based on the measured data reached 95.2%. The experimental results demonstrate that the proposed method is effective in the measured data and the micro-Doppler signature is effective for dynamic HGR.