Title
Enhancing augmented reality with machine learning for hands-on origami training
Authors
[ 1 ] Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ 2 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee
Scientific discipline (Law 2.0)
[2.2] Automation, electronics, electrical engineering and space technologies[2.9] Mechanical engineering
Year of publication
2025
Published in
Article type
scientific article
Publication language
english
Keywords
EN
- virtual reality
- metaverse
- origami
- manual training
- machine learning
- user tests
Abstract
EN This research explores integrating augmented reality (AR) with machine learning (ML) to enhance hands-on skill acquisition through origami folding. We developed an AR system using the YOLOv8 model to provide real-time feedback and automatic validation of each folding step, offering step-by-step guidance to users. A novel approach to training dataset preparation was introduced, which improves the accuracy of detecting and assessing origami folding stages. In a formative user study involving 16 participants tasked with folding multiple origami models, the results revealed that while the ML-driven feedback increased task completion times, it also made participants feel more confident throughout the folding process. However, they also reported that the feedback system added cognitive load, slowing their progress, though it provided valuable guidance. These findings suggest that while ML-supported AR systems can enhance the user experience, further optimization is required to streamline the feedback process and improve efficiency in complex manual tasks.
Pages (from - to)
1499830-01 - 1499830-14
Comments
Article number: 1499830
License type
CC BY (attribution alone)
Open Access Mode
open journal
Open Access Text Version
final published version
Date of Open Access to the publication
at the time of publication
Full text of article
Access level to full text
public
Ministry points / journal
5
Impact Factor
3,6 [List 2024]
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