Automated inference of cognitive performance by fusing multimodal information acquired by smartphone

Takashi Hamatani, Keiichi Ochiai, Akiya Inagaki, Naoki Yamamoto, Yusuke Fukazawa, Masatoshi Kimoto, Kazuki Kiriu, Kouhei Kaminishi, Jun Ota, Yuri Terasawa, Tsukasa Okimura, Takaki Maeda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recognizing human cognitive performance is important for preserving working efficiency and preventing human error. This paper presents a method for estimating cognitive performance by leveraging multiple information available in a smartphone. The method employs the Go-NoGo task to measure cognitive performance, and fuses contextual and behavioral features to identify the level of performance. It was confirmed that the proposed method could recognize whether cognitive performance was high or low with an average accuracy of 71%, even when only referring to inertial sensor logs. Combining sensing modalities improved the accuracy up to 74%.

Original languageEnglish
Title of host publicationUbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery, Inc
Pages921-928
Number of pages8
ISBN (Electronic)9781450368698
DOIs
Publication statusPublished - 2019 Sep 9
Event2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019 - London, United Kingdom
Duration: 2019 Sep 92019 Sep 13

Publication series

NameUbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers

Conference

Conference2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019
CountryUnited Kingdom
CityLondon
Period19/9/919/9/13

Keywords

  • Cognitive performance
  • Go-NoGo task
  • Machine learning
  • Smartphone log

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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  • Cite this

    Hamatani, T., Ochiai, K., Inagaki, A., Yamamoto, N., Fukazawa, Y., Kimoto, M., Kiriu, K., Kaminishi, K., Ota, J., Terasawa, Y., Okimura, T., & Maeda, T. (2019). Automated inference of cognitive performance by fusing multimodal information acquired by smartphone. In UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (pp. 921-928). (UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers). Association for Computing Machinery, Inc. https://doi.org/10.1145/3341162.3346275