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).
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  • 2015
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  • 2019-11-18
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