The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Décrire avec précision les comportements des utilisateurs avec des capteurs appropriés est toujours important lors du développement de systèmes informatiques rentables. Cet article utilise des ensembles de données enregistrés pour la détection de lectures fines à l'aide du J!NS MEME, un dispositif de lunettes doté de capteurs d'électrooculographie (EOG), d'accéléromètre et de gyroscope. Nous générons des modèles pour toutes les combinaisons possibles des trois capteurs et utilisons l'apprentissage auto-supervisé et l'apprentissage supervisé afin de comprendre les paramètres optimaux des capteurs. Les résultats montrent que seul le capteur EOG fonctionne à peu près aussi bien que la combinaison la plus performante d'autres capteurs. Cela donne un aperçu de la sélection des capteurs appropriés pour une détection de lecture fine, permettant un calcul rentable.
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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Md. Rabiul ISLAM, Andrew W. VARGO, Motoi IWATA, Masakazu IWAMURA, Koichi KISE, "Exploring Sensor Modalities to Capture User Behaviors for Reading Detection" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 9, pp. 1629-1633, September 2022, doi: 10.1587/transinf.2020ZDL0003.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020ZDL0003/_p
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@ARTICLE{e105-d_9_1629,
author={Md. Rabiul ISLAM, Andrew W. VARGO, Motoi IWATA, Masakazu IWAMURA, Koichi KISE, },
journal={IEICE TRANSACTIONS on Information},
title={Exploring Sensor Modalities to Capture User Behaviors for Reading Detection},
year={2022},
volume={E105-D},
number={9},
pages={1629-1633},
abstract={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.},
keywords={},
doi={10.1587/transinf.2020ZDL0003},
ISSN={1745-1361},
month={September},}
Copier
TY - JOUR
TI - Exploring Sensor Modalities to Capture User Behaviors for Reading Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1629
EP - 1633
AU - Md. Rabiul ISLAM
AU - Andrew W. VARGO
AU - Motoi IWATA
AU - Masakazu IWAMURA
AU - Koichi KISE
PY - 2022
DO - 10.1587/transinf.2020ZDL0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2022
AB - 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.
ER -