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Understanding and improving mobile reading via scalable and low cost sensing

Guo, Wei (2019) Understanding and improving mobile reading via scalable and low cost sensing. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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In recent years, due to the increasing ubiquity of Internet and mobile devices, mobile reading on smart watches and smartphones is experiencing rapid growth. Despite the great potential, new challenges are brought. Compared to traditional reading, mobile reading faces major challenges such as encountering more frequent distractions and lacking portable and efficient technique to deeply understand and improve it.
Fortunately, the development of the hardware and software of mobile devices provide an opportunity to track users’ behavior and physiological signals accurately in a low-cost and portable manner. In this thesis, I explored the usage of low-cost mobile sensors to solve the measurement challenges of reading.
I used the low-cost mobile sensing techniques on mobile devices to understand and improve the degree and quality of reading. In this thesis, I first present SmartRSVP, a reading interface on smart watches that leverages eye-gaze contact tracking technique and heart rate sensing technique to facilitate reading under distractions. I then present Lepton, an intelligent reading system on smart phones that tracks eye-gaze periodical patterns and sensing the screen touching behavior to monitor readers’ cognitions and emotions during reading. Lastly, I present StrategicReading, which uses the implicitly captured eye gaze patterns, scrolling motions, and log histories to monitor users’ reading strategies and performance during multiple-sources online reading.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Guo, Weiweg21@pitt.eduweg21
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKovashka,
Committee CoChairWang,
Committee MemberLitman, Diane J.dlitman@pitt.eduDLITMAN
Committee MemberCho,
Date: 29 July 2019
Date Type: Publication
Defense Date: 25 April 2019
Approval Date: 29 July 2019
Submission Date: 7 June 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 110
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Human Computer Interactions, Intelligent user interface, Machine learning, Mobile computing, Text reading
Date Deposited: 29 Jul 2019 13:41
Last Modified: 29 Jul 2019 13:41


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