Accurate drowsiness estimation via eye-related movements: a neural-network-based investigation

DOI

Bibliographic Information

Other Title
  • 眼に関連した動きからの高精度な眠気推定：ニューラルネットワークに基づく調査研究

Description

<p>Many studies reported eye-related movements, e.g., eye blink and eyelid drooping, are highly indicative symptoms of drowsiness. However, few has investigated the computational efficacy for drowsiness estimation accounted by these movements. This paper thus analyzes two typical movements: eyelid movements and eyeball movements, and investigates different neural-network modelings: CNN-Net and CNN-LSTM-Net. Experimental results show that using joint movements can achieve better performances than eyelid movements for short time drowsiness estimation while using eyeball movements alone perform worse even than the baseline (PERCLOS method). In addition, the CNN-Net is more effective for accurate drowsiness level estimation than the CNN-LSTM-Net.</p>

Journal

Details 詳細情報について

  • CRID
    1390001288048993152
  • NII Article ID
    130007426378
  • DOI
    10.11517/pjsai.jsai2018.0_3pin149
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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