Detecting learner drowsiness based on facial expressions and head movements in online courses

概要

Drowsiness is a major factor that hinders learning. To improve learning efficiency, it is important to understand students’ physical status such as wakefulness during online coursework. In this study, we have proposed a drowsiness estimation method based on learners’ head and facial movements while viewing video lectures. To examine the effectiveness of head and facial movements in drowsiness estimation, we collected learner video data recorded during e-learning and applied a deep learning approach under the following conditions: (a) using only facial movement data, (b) using only head movement data, and (c) using both facial and head movement data.We achieved an average F1-macro score of 0.74 in personalized models for detecting learner drowsiness using both facial and head movement data.

武村紀子

招へい准教授

パターン認識、機械学習等を用いた環境知能や歩容認証等に関する研究に従事。

長原一

教授

コンピューテーショナルフォトグラフィ、コンピュータビジョンを専門とし実世界センシングや情報処理技術、画像認識技術の研究を行う。さらに、画像センシングにとどまらず様々なセンサに拡張したコンピュテーショナルセンシング手法の開発や高次元で冗長な実世界ビッグデータから意味のある情報を計測するスパースセンシングへの転換を目指す。