TOWARDS ACTIVITY RECOGNITION OF LEARNERS IN ON-LINE LECTURE

Authors

  • HIROMICHI ABE Kyushu University Motooka 744, Nishi-ku, Fukuoka, 819-0395, Japan
  • TAKUYA KAMIZONO Kyushu University Motooka 744, Nishi-ku, Fukuoka, 819-0395, Japan
  • KAZUYA KINOSHITA Kyushu University Motooka 744, Nishi-ku, Fukuoka, 819-0395, Japan
  • KENSUKE BABA Kyushu University Motooka 744, Nishi-ku, Fukuoka, 819-0395, Japan
  • SHIGERU TAKANO Kyushu University Motooka 744, Nishi-ku, Fukuoka, 819-0395, Japan
  • KAZUAKI MURAKAMI Kyushu University Motooka 744, Nishi-ku, Fukuoka, 819-0395, Japan

Keywords:

Activity recognition, Kinect, data mining, e-learning

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.

 

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Published

2015-03-29

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Articles