abstract: This work solves the problem of incorrect rotations while using handheld devices.Two new methods which improve upon previous works are explored. The first method
uses an infrared camera to capture and detect the user’s face position and orient the
display accordingly. The second method utilizes gyroscopic and accelerometer data
as input to a machine learning model to classify correct and incorrect rotations.
Experiments show that these new methods achieve an overall success rate of 67%
for the first and 92% for the second which reaches a new high for this performance
category. The paper also discusses logistical and legal reasons for implementing this
feature into an end-user product from a business perspective. Lastly, the monetary
incentive behind a feature like irRotate in a consumer device and explore related
patents is discussed. / Dissertation/Thesis / Masters Thesis Computer Science 2020
| Identifer | oai:union.ndltd.org:asu.edu/item:62936 |
| Date | January 2020 |
| Contributors | Tallman, Riley (Author), Yang, Yezhou (Advisor), Liang, Jianming (Committee member), Chen, Yinong (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 52 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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