Towards activity recognition of learners in on-line lecture

Hiromichi Abe, Takuya Kamizono, Kazuya Kinoshita, Kensuke Baba, Shigeru Takano, Kazuaki Murakami

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Understanding the states of learners at a lecture is useful for improving the quality of the lecture. A video camera with an infrared sensor Kinect has been widely studied and proved to be useful for some kinds of activity recognition. However, learners in a lecture usually do not act with large moving. This paper evaluates Kinect for use of activity recognition of learners. The authors considered four activities for detecting states of a learner in an on-line lecture, and collected the data with the activities by a Kinect. They repaired the collected data by padding some lacks, and then applied machine learning methods to the data. As the result, they obtained the accuracy 0.985 of the activity recognition. The result shows that Kinect is applicable also to the activity recognition of learners in an on-line lecture.

Original languageEnglish
Pages (from-to)205-212
Number of pages8
JournalJournal of Mobile Multimedia
Volume11
Issue number3-4
Publication statusPublished - Nov 30 2015
Externally publishedYes

Keywords

  • Activity recognition
  • Data mining
  • E-learning
  • Kinect

ASJC Scopus subject areas

  • Communication
  • Media Technology
  • Industrial and Manufacturing Engineering

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