Date of Award

Spring 1-1-2015

Document Type

Thesis

Degree Name

Master of Science (MS)

First Advisor

Tom Yeh

Second Advisor

Peter Mathys

Third Advisor

Shaun Kane

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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