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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

  • Czech description

Classification

  • Type

    J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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

  • EID of the result in the Scopus database

    2-s2.0-85173683350