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Attention Sensitive Web Browsing

Authors:

Anish Anil Patankar,

Ankit KumarAuthors Info & Claims

COMPUTE '16: Proceedings of the 9th Annual ACM India Conference

Pages 147 - 152

https://doi.org/10.1145/2998476.2998494

Published: 21 October 2016 Publication History

Abstract

The attention level is an important indicator of the user's level of interest while viewing any content. The web browser is one of the most popular means to access information, and the usage of browsers in mobile devices is increasing. In this paper we analyze the use of attention as an input for web browsers. Attention can be measured easily in real time using cheap commercially available wearable EEG sensors, such as NeuroSky's MindWave. We use the measured level of attention in the following ways: as an input mechanism for navigating through the controls on the web browser such as buttons, menus and hyperlinks, to correlate the attention with the section of the webpage being browsed and make the web browser responsive to the user's attention level in real time, and as an input that is fed back to the web server enabling the web content developer to make attention sensitive websites. For each of these, we provide the implementation details and some results obtained. We also provide some pointers how the input attention level event obtained from the EEG sensors can be standardized in the W3C specification.

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Cited By

Camilleri APorter CCamilleri T(2022)Boggle: An SSVEP-Based BCI Web BrowserComputer-Human Interaction Research and Applications10.1007/978-3-031-22015-9_6(100-123)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-22015-9_6
Sarsam SAl-Samarraie H(2018)A First Look at the Effectiveness of Personality Dimensions in Promoting Users’ Satisfaction With the SystemSage Open10.1177/21582440187691258:2Online publication date: 11-Apr-2018
https://doi.org/10.1177/2158244018769125
Prathibha RSwetha LShobha K(2017)Brain computer interface: Design and development of a smart robotic gripper for a prosthesis environment2017 International Conference on Networks & Advances in Computational Technologies (NetACT)10.1109/NETACT.2017.8076780(278-283)Online publication date: Jul-2017
https://doi.org/10.1109/NETACT.2017.8076780
Show More Cited By

Index Terms

Attention Sensitive Web Browsing
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Other conferences

COMPUTE '16: Proceedings of the 9th Annual ACM India Conference

October 2016

178 pages

ISBN:9781450348089

DOI:10.1145/2998476

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2016

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ACM COMPUTE '16

ACM COMPUTE '16: Ninth Annual ACM India Conference

October 21 - 23, 2016

Gandhinagar, India

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COMPUTE '16 Paper Acceptance Rate 22 of 117 submissions, 19%;

Overall Acceptance Rate 114 of 622 submissions, 18%

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Cited By

Camilleri APorter CCamilleri T(2022)Boggle: An SSVEP-Based BCI Web BrowserComputer-Human Interaction Research and Applications10.1007/978-3-031-22015-9_6(100-123)Online publication date: 13-Dec-2022
https://doi.org/10.1007/978-3-031-22015-9_6
Sarsam SAl-Samarraie H(2018)A First Look at the Effectiveness of Personality Dimensions in Promoting Users’ Satisfaction With the SystemSage Open10.1177/21582440187691258:2Online publication date: 11-Apr-2018
https://doi.org/10.1177/2158244018769125
Prathibha RSwetha LShobha K(2017)Brain computer interface: Design and development of a smart robotic gripper for a prosthesis environment2017 International Conference on Networks & Advances in Computational Technologies (NetACT)10.1109/NETACT.2017.8076780(278-283)Online publication date: Jul-2017
https://doi.org/10.1109/NETACT.2017.8076780
Maiti AVeeradhi HSaha S(2017)Thought Co-Relation: A Quantitative Approach to Classify EEG Data for Predictive AnalysisProgress in Advanced Computing and Intelligent Engineering10.1007/978-981-10-6875-1_13(127-136)Online publication date: 22-Dec-2017
https://doi.org/10.1007/978-981-10-6875-1_13

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