Graduate Thesis Or Dissertation
Detecting User Frustration From Smartphone Sensors: A Multimodal Classification Approach Public Deposited
https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/kh04dq067
- Abstract
- Most smartphone applications are unaware that users feel frustrated by a bug, an error, or a usability problem. Could a smartphone be “smart” enough to detect that its user became frustrated by something? A pilot study was conducted as proof of concept, showing that sensor readings collected in the background from a smartphone could be used to detect user frustration after the onset of a frustrating event due to a bug or a usability problem. Twenty-one participants were asked to perform a series of multitasking tasks, during which errors were purposely introduced to frustrate them. Sensor data, including motion, touch, and camera, were collected and used to train a binary classifier that is able to detect frustration with reasonable accuracy when merging data from different modalities (motion sensors and touch gestures).
- Creator
- Date Issued
- 2015
- Academic Affiliation
- Advisor
- Committee Member
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- Commencement Year
- Subject
- Last Modified
- 2019-11-18
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Thumbnail | Title | Date Uploaded | Visibility | Actions |
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detectingUserFrustrationFromSmartphoneSensorsAMultimodal.pdf | 2019-11-18 | Public | Download |