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Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning.

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  • المؤلفون: Song S;Song S; Kim B; Kim B; Kim S; Kim S; Kim S; Lee J; Lee J; Lee J
  • المصدر:
    Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Jun 07; Vol. 21 (11). Date of Electronic Publication: 2021 Jun 07.
  • نوع النشر :
    Journal Article
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • الموضوع:
      Data Compression* ; Deep Learning*; Algorithms ; Gestures ; Pattern Recognition, Automated ; Radar
    • نبذة مختصرة :
      Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar's inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.
    • References:
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    • Grant Information:
      20-IT-02 DGIST R&D Program of the Ministry of Science, ICT and Future 283 Planning, Korea
    • Contributed Indexing:
      Keywords: AlexNet; CNN; Doppler radar; STFT; SVD; deep learning; foot gesture; gesture recognition
    • الموضوع:
      Date Created: 20210702 Date Completed: 20210707 Latest Revision: 20210707
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC8201004
    • الرقم المعرف:
      10.3390/s21113937
    • الرقم المعرف:
      34200461