Guo, Wei
(2019)
Understanding and improving mobile reading via scalable and low cost sensing.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
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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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| 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 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/36903 |
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