Towards activity recognition of learners by simple electroencephalographs

Hiromichi Abe; Kensuke Baba; Sigeru Takano; Kazuaki Murakami

doi:10.1145/2618168.2618194

Towards activity recognition of learners by simple electroencephalographs

Hiromichi Abe, Kensuke Baba, Sigeru Takano, Kazuaki Murakami

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Understanding the states of learners at a lecture is expected to be useful for improving the quality of the lecture. This paper investigates the possibility of use of a simple electroencephalograph MindTune for activity recognition of a learner. The authors considered three kinds of activities for detecting states of a learner, and collected electroencephalography data with the activities by MindTune. Then, they applied K-nearest neighbor algorithm to the collected data, and the accuracy of the activity recognition was 58.2%. The result indicates a possibility of using MindTune for the activity recognition of learners.

Original language	English
Title of host publication	Proceedings of International Conference on Information Systems and Design of Communication, ISDOC 2014
Publisher	Association for Computing Machinery
Pages	161-164
Number of pages	4
ISBN (Print)	9781450327138
DOIs	https://doi.org/10.1145/2618168.2618194
Publication status	Published - 2014
Externally published	Yes
Event	International Conference on Information Systems and Design of Communication, ISDOC 2014 - Lisbon, Portugal Duration: May 16 2014 → May 17 2014

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	International Conference on Information Systems and Design of Communication, ISDOC 2014
Country/Territory	Portugal
City	Lisbon
Period	5/16/14 → 5/17/14

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/2618168.2618194

Cite this

Abe, H., Baba, K., Takano, S., & Murakami, K. (2014). Towards activity recognition of learners by simple electroencephalographs. In Proceedings of International Conference on Information Systems and Design of Communication, ISDOC 2014 (pp. 161-164). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/2618168.2618194

Towards activity recognition of learners by simple electroencephalographs. / Abe, Hiromichi; Baba, Kensuke; Takano, Sigeru et al.

Proceedings of International Conference on Information Systems and Design of Communication, ISDOC 2014. Association for Computing Machinery, 2014. p. 161-164 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abe, H, Baba, K, Takano, S & Murakami, K 2014, Towards activity recognition of learners by simple electroencephalographs. in Proceedings of International Conference on Information Systems and Design of Communication, ISDOC 2014. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 161-164, International Conference on Information Systems and Design of Communication, ISDOC 2014, Lisbon, Portugal, 5/16/14. https://doi.org/10.1145/2618168.2618194

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abstract = "Understanding the states of learners at a lecture is expected to be useful for improving the quality of the lecture. This paper investigates the possibility of use of a simple electroencephalograph MindTune for activity recognition of a learner. The authors considered three kinds of activities for detecting states of a learner, and collected electroencephalography data with the activities by MindTune. Then, they applied K-nearest neighbor algorithm to the collected data, and the accuracy of the activity recognition was 58.2%. The result indicates a possibility of using MindTune for the activity recognition of learners.",

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AB - Understanding the states of learners at a lecture is expected to be useful for improving the quality of the lecture. This paper investigates the possibility of use of a simple electroencephalograph MindTune for activity recognition of a learner. The authors considered three kinds of activities for detecting states of a learner, and collected electroencephalography data with the activities by MindTune. Then, they applied K-nearest neighbor algorithm to the collected data, and the accuracy of the activity recognition was 58.2%. The result indicates a possibility of using MindTune for the activity recognition of learners.

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Towards activity recognition of learners by simple electroencephalographs

Abstract

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ASJC Scopus subject areas

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