Pulsed Millimeter Wave Radar for Hand Gesture Sensing and Classification

Lars Ohlsson Fhager, Sebastian Heunisch, Hannes Dahlberg, Anton Evertsson, Lars Erik Wernersson

Research output: Contribution to journalLetterpeer-review

12 Citations (SciVal)

Abstract

A pulsed millimeter wave radar operating at a frame rate of 144 Hz is utilized to record 2160 scattering signatures of 12 generic hand gestures. Gesture recognition is achieved by machine learning, utilizing transfer learning on a pretrained convolutional neural network. This yields excellent classification results with a validation accuracy of 99.5%, based on a 60% training versus 40% validation split. The corresponding confusion matrix is also presented, showing a high level of classification orthogonality between the tested gestures. This is the first demonstration where data from a pulsed millimeter wave radar is used for gesture recognition by machine learning. It proves that the range-time envelope representation of high frame-rate data from a pulsed radar is suitable for hand gesture recognition. Further improvements are expected for more complex detection schemes and tailored neural networks.

Original languageEnglish
Article number3502404
JournalIEEE Sensors Letters
Volume3
Issue number12
DOIs
Publication statusPublished - 2019 Dec

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)

Keywords

  • classification
  • convolutional neural network
  • gesture sensing
  • hand gesture recognition
  • machine learning
  • Microwave/millimeter wave sensors
  • millimeter wave radar
  • pulsed radar
  • transfer learning

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