[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127104-en":3,"doc-seo-127104-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127104,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing augmented reality with machine learning for hands-on origami training - Research Article","Exploring the integration of augmented reality (AR) with machine learning (ML) to strengthen hands-on skill acquisition through origami folding, the work presents an AR system that uses YOLOv8 for real-time feedback and automatic validation of each folding step. A new training-dataset preparation strategy is introduced to improve accuracy in detecting and assessing origami folding stages. In a formative user study with 16 participants, ML-driven feedback increased task completion time but improved perceived confidence while adding cognitive load, indicating the need for feedback-streamlining to improve efficiency.","TYPE Original Research PUBLISHED 27 January 2025 DOI 10.3389/frvir.2025.1499830  \nOPEN ACCESS  \nEDITED BY  \nAndrea Sanna,  \nPolytechnic University of Turin, Italy  \nREVIEWED BY  \nAikaterini Bourazeri,  \nUniversity of Essex, United Kingdom Lia Morra,  \nPolytechnic University of Turin, Italy  \n*CORRESPONDENCE  \nMikołaj Łysakowski,  \n [mikolaj.lysakowski@put.poznan.pl](mikolaj.lysakowski@put.poznan.pl)  \nRECEIVED 21 September 2024  \nACCEPTED 07 January 2025  \nPUBLISHED 27 January 2025  \nCITATION  \nŁysakowski M, Gapsa J, Lyu C, Bohné T, Tadeja SK and Skrzypczyński P (2025)  \nEnhancing augmented reality with machine learning for hands-on origami training.  \nFront. Virtual Real. 6:1499830 .  \ndoi: 10.3389/frvir.2025.1499830  \nCOPYRIGHT  \n© 2025 Łysakowski, Gapsa, Lyu, Bohné, Tadeja and Skrzypczyński. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEnhancing augmented reality with machine learning for hands-on origami training  \nMikołaj Łysakowski1*, Jakub Gapsa 2, Chenxu Lyu 3, Thomas Bohné 3, Sławomir Konrad Tadeja3 and Piotr Skrzypczyński1,4  \n1Center for Artificial Intelligence and Cybersecurity, Poznań University of Technology, Poznań, Poland, 2Faculty of Mechanical Engineering, Poznań University of Technology, Poznań, Poland, 3Department of Engineering, University of Cambridge, Cambridge, United Kingdom, 4Institute of Robotics and Machine Intelligence, Poznań University of Technology, Poznań, Poland  \nThis 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.  \nKEYWORDS  \naugmented reality, machine learning, edge computing, assembly task, education  \n1 Introduction  \nAugmented reality (AR) is increasingly transforming education by enabling immersive, hands-on learning experiences, particularly in scenarios where human instructors are unavailable or traditional learning environments are inadequate (Zonaphan et al., 2022). By overlaying digital content onto the physical world, AR facilitates interactive and engaging training, making it a powerful tool for skill development in both education and practical applications (Zambri and Kamaruzaman, 2020) .  \nThe integration of AR with machine learning (ML) introduces new possibilities for automated feedback in manual skill acquisition. Building on our previous work (Łysakowski et al., 2024), we explore how an AR system powered by a YOLOv8 model can provide realtime detection and evaluation of user actions, specifically within the context of origami folding. The novelty of this research lies in the deployment of a state-of-the-art object detection algorithm on a resource-constrained AR device (HoloLens 2) to ","cbCaivUtX1uAyco9","https://ap.wps.com/l/cbCaivUtX1uAyco9","pdf",2132188,1,14,"English","en",105,"# Introduction\n## AR and education\n## AR with machine learning for automated feedback\n## Origami as a training domain\n## Related work and limitations","[{\"question\":\"What system is proposed for training origami with augmented reality and machine learning?\",\"answer\":\"An AR system uses the YOLOv8 object-detection model to provide real-time feedback and to automatically validate each user folding step during origami training.\"},{\"question\":\"How does the paper improve the model’s ability to assess origami folding stages?\",\"answer\":\"It introduces a novel approach to preparing the training dataset, improving the accuracy of detecting and evaluating origami folding stages.\"},{\"question\":\"What were the main outcomes of the user study with 16 participants?\",\"answer\":\"ML-driven feedback increased task completion times but also increased participants’ confidence. Participants reported higher cognitive load, which slowed their progress, even though the feedback provided valuable guidance.\"}]","Enhancing augmented reality with machine learning for hands-on origami training - Research Article | PDF",1785936864,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-augmented-reality-with-machine-learning-for-hands-on-origami-training-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-augmented-reality-with-machine-learning-for-hands-on-origami-training-research-article/127104/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What system is proposed for training origami with augmented reality and machine learning?","Question",{"text":75,"@type":76},"An AR system uses the YOLOv8 object-detection model to provide real-time feedback and to automatically validate each user folding step during origami training.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper improve the model’s ability to assess origami folding stages?",{"text":80,"@type":76},"It introduces a novel approach to preparing the training dataset, improving the accuracy of detecting and evaluating origami folding stages.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main outcomes of the user study with 16 participants?",{"text":84,"@type":76},"ML-driven feedback increased task completion times but also increased participants’ confidence. 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