uxSense: Supporting User Experience Analysis with Visualization and Computer Vision

Analyzing user behavior from usability evaluation can be a challenging and time-consuming task, especially as the number of participants and the scale and complexity of the evaluation grows. We propose uxSense , a visual analytics system using machine learning methods to extract user behavior from a...

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Vydáno v:IEEE transactions on visualization and computer graphics Ročník 30; číslo 7; s. 3841 - 3856
Hlavní autoři: Batch, Andrea, Ji, Yipeng, Fan, Mingming, Zhao, Jian, Elmqvist, Niklas
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Annotations
Audio data
Computer vision
Data transmission
Data visualization
deep learning
evaluation
Feature extraction
Gaze tracking
Machine learning
Measurement
Natural language processing
Pattern recognition
Usability
User experience
video analytics
Visual analytics
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
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Shrnutí:Analyzing user behavior from usability evaluation can be a challenging and time-consuming task, especially as the number of participants and the scale and complexity of the evaluation grows. We propose uxSense , a visual analytics system using machine learning methods to extract user behavior from audio and video recordings as parallel time-stamped data streams. Our implementation draws on pattern recognition, computer vision, natural language processing, and machine learning to extract user sentiment, actions, posture, spoken words, and other features from such recordings. These streams are visualized as parallel timelines in a web-based front-end, enabling the researcher to search, filter, and annotate data across time and space. We present the results of a user study involving professional UX researchers evaluating user data using uxSense. In fact, we used uxSense itself to evaluate their sessions.
Bibliografie:ObjectType-Article-1
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2023.3241581