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Exploring Sensor Modalities to Capture User Behaviors for Reading Detection

Md. Rabiul ISLAM, Andrew W. VARGO, Motoi IWATA, Masakazu IWAMURA, Koichi KISE

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Summary :

Accurately describing user behaviors with appropriate sensors is always important when developing computing cost-effective systems. This paper employs datasets recorded for fine-grained reading detection using the J!NS MEME, an eye-wear device with electrooculography (EOG), accelerometer, and gyroscope sensors. We generate models for all possible combinations of the three sensors and employ self-supervised learning and supervised learning in order to gain an understanding of optimal sensor settings. The results show that only the EOG sensor performs roughly as well as the best performing combination of other sensors. This gives an insight into selecting the appropriate sensors for fine-grained reading detection, enabling cost-effective computation.

Publication
IEICE TRANSACTIONS on Information Vol.E105-D No.9 pp.1629-1633
Publication Date
2022/09/01
Publicized
2022/06/20
Online ISSN
1745-1361
DOI
10.1587/transinf.2020ZDL0003
Type of Manuscript
LETTER
Category
Human-computer Interaction

Authors

Md. Rabiul ISLAM
  Osaka Prefecture University,BSMRSTU
Andrew W. VARGO
  Osaka Prefecture University
Motoi IWATA
  Osaka Prefecture University
Masakazu IWAMURA
  Osaka Prefecture University
Koichi KISE
  Osaka Prefecture University

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