DarkReader: Bridging the gap between perception and reality of power consumption in smartphones for blind users

Jian Xu, Syed Masum Billah, Aruna Balasubramanian, Roy Shilkrot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This paper presents a user study with 10 blind participants to understand their perception of power consumption in smartphones. We found that a widely used power saving mechanism for smartphones-pressing the power button to put the smartphone to sleep-has a serious usability issue for blind screen reader users. Among other fndings, our study also unearthed several usage patterns and misconceptions of blind users that contribute to excessive battery drainage. Informed by the frst user study, this paper proposes DarkReader, a screen reader developed in Android that bridges users' perception of power consumption to reality. DarkReader darkens the screen by truly turning it off, but allows users to interact with their smartphones. A second user study with 10 blind participants shows that participants perceived no difference in completion times in performing routine tasks using DarkReader and default screen reader. Yet DarkReader saves 24% to 52% power depending on tasks and screen brightness.

Original languageEnglish (US)
Title of host publicationASSETS 2019 - 21st International ACM SIGACCESS Conference on Computers and Accessibility
PublisherAssociation for Computing Machinery, Inc
Pages96-104
Number of pages9
ISBN (Electronic)9781450366762
DOIs
StatePublished - Oct 24 2019
Event21st International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2019 - Pittsburgh, United States
Duration: Oct 28 2019Oct 30 2019

Publication series

NameASSETS 2019 - 21st International ACM SIGACCESS Conference on Computers and Accessibility

Conference

Conference21st International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2019
Country/TerritoryUnited States
CityPittsburgh
Period10/28/1910/30/19

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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