Reducing Smartwatch Users’ Distraction with Convolutional Neural Network
Joint Authors
Lee, Jemin
Kim, Hyungshin
Kwon, Jinse
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-15
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Telecommunications Engineering
Abstract EN
Smartwatches provide a useful feature whereby users can be directly aware of incoming notifications by vibration.
However, such prompt awareness causes high distractions to users.
To remedy the distraction problem, we propose an intelligent notification management for smartwatch users.
The goal of our management system is not only to reduce the annoying notifications but also to provide the important notifications that users will swiftly react to.
To analyze how to respond to the notifications daily, we have collected 20,353 in-the-wild notifications.
Subsequently, we trained the convolutional neural network models to classify important notifications according to the users’ contexts.
Finally, the proposed management allows important notifications to be forwarded to a smartwatch.
As experiment results show, the proposed method can reduce the number of unwanted notifications on smartwatches by up to 81%.
American Psychological Association (APA)
Lee, Jemin& Kwon, Jinse& Kim, Hyungshin. 2018. Reducing Smartwatch Users’ Distraction with Convolutional Neural Network. Mobile Information Systems،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1204958
Modern Language Association (MLA)
Lee, Jemin…[et al.]. Reducing Smartwatch Users’ Distraction with Convolutional Neural Network. Mobile Information Systems No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1204958
American Medical Association (AMA)
Lee, Jemin& Kwon, Jinse& Kim, Hyungshin. Reducing Smartwatch Users’ Distraction with Convolutional Neural Network. Mobile Information Systems. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1204958
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204958