UX and Machine Learning – Preprocessing of Audiovisual Data Using Computer Vision to Recognize UI Elements
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F23%3A96163" target="_blank" >RIV/60460709:41110/23:96163 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.7160/aol.2023.150304" target="_blank" >https://doi.org/10.7160/aol.2023.150304</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7160/aol.2023.150304" target="_blank" >10.7160/aol.2023.150304</a>
Alternative languages
Result language
angličtina
Original language name
UX and Machine Learning – Preprocessing of Audiovisual Data Using Computer Vision to Recognize UI Elements
Original language description
This study explores the convergence of user experience (UX) and machine learning, particularly employing computer vision techniques to preprocess audiovisual data to detect user interface (UI) elements. With an emphasis on usability testing, the study introduces a novel approach for recognizing changes in UI screens within video recordings. The methodology involves a sequence of steps, including form prototype creation, laboratory experiments, data analysis, and computer vision tasks. The future aim is to automate the evaluation of user behavior during UX testing. This innovative approach is relevant to the agricultural domain, where specialized applications for precision agriculture, subsidy requests, and production reporting demand streamlined usability. The research introduces a frame extraction algorithm that identifies screen changes by analyzing pixel differences between consecutive frames. Additionally, the study employs YOLOv7, an efficient object detection model, to identify UI elements within the video frames. Results showcase successful screen change detection with minimal false negatives and acceptable false positives, showcasing the potential for enhanced automation in UX testing. The study’s implications lie in simplifying analysis processes, enhancing insights for design decisions, and fostering user-centric advancements in diverse sectors, including precision agriculture.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
AGRIS on-line Papers in Economics and Informatics
ISSN
1804-1930
e-ISSN
1804-1930
Volume of the periodical
15
Issue of the periodical within the volume
03/2023
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
10
Pages from-to
35-44
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85173683350