Reducing Smartwatch Users’ Distraction with Convolutional Neural Network

Joint Authors

Lee, Jemin
Kim, Hyungshin
Kwon, Jinse

Source

Mobile Information Systems

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