[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127107-en":3,"doc-seo-127107-105":30,"detail-sidebar-cat-0-en-105":83},{"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},127107,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing Augmented Reality with Machine Learning for Hands-On Origami Training - Research Summary","This research explores integrating augmented reality (AR) with machine learning (ML) to strengthen hands-on skill acquisition through origami folding. An AR system is built with the YOLOv8 model for real-time feedback and automatic validation of each folding step, supported by a new training dataset preparation approach. A formative study with 16 participants shows ML-driven feedback increases confidence yet adds cognitive load and can slow completion times, requiring further optimization for efficiency.","Enhancing Augmented Reality with Machine Learning for Hands-On Origami Training  \nMikołaj Łysakowski 1 ,∗ , Jakub Gapsa 3 , Chenxu Lyu4 , Thomas Bohné4 , Sławomir Tadeja 4 and Piotr Skrzypczy ski 1 ,2  \nPoznan´ University of Technology: 1 Center for Artificial Intelligence and Cybersecurity, 2 Institute of Robotics and Machine Intelligence, 3 Faculty of Mechanical Engineering, Poznan´, Poland,  \n3 Department of Engineering, University of Cambridge, Cambridge, UK  \nCorrespondence*:  \nMikołaj Łysakowski [mikolaj.lysakowski@put.poznan.pl](mikolaj.lysakowski@put.poznan.pl)  \nABSTRACT  \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-bystep 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: augmented 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 real-time 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 enable on-device step validation without requiring external computation. Unlike previous systems, which focus on predefined animations or step sequences, our approach evaluates both user actions and outcomes in realtime, directly influencing the learning process.  \nFigure 1. A user wearing a HoloLens 2 head-mounted display while folding an origami model (a), and screenshots from the AR application showing: (b) an animated model guiding the folding process,(c) a correctly completed folding step, and (d) an incorrectly completed folding step.  \nOrigami, the Japanese art of paper folding, is known to enhance manual and cognitive skills by improving fine motor abilities, hand-eye coordination, and spatial awareness (Supple et al., 2021) . These benefits make origami a valuable tool in education, therapy, and personal development, with research supporting its effectiveness across various fields (Zhao et al., 2020) . For instance, Herbas Torrico (2021) highlighted how origami was used during COVID-19 lockdowns as an educational tool, emphasizing its role in cognitive development. The pandemic also revealed origami’s versatility, with Monnier and Winters (2022) demonstrating its calming and creative benefits during the lo","cbCaid6YhpENgfI0","https://ap.wps.com/l/cbCaid6YhpENgfI0","pdf",24108983,1,21,"English","en",105,"# Introduction\n## AR in education and training\n## AR-ML integration for automated feedback\n# Related Work\n## Immersive technologies and skill practice","[{\"question\":\"Why is optimization still needed after introducing ML-supported AR feedback?\",\"answer\":\"Further optimization is required to streamline the feedback process and improve efficiency, especially for complex manual tasks.\"}]","Enhancing Augmented Reality with Machine Learning for Hands-On Origami Training - Research Summary | PDF",1785936874,53,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"enhancing-augmented-reality-with-machine-learning-for-hands-on-origami-training-research-summary","",{"@graph":36,"@context":77},[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-summary/127107/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Why is optimization still needed after introducing ML-supported AR feedback?","Question",{"text":75,"@type":76},"Further optimization is required to streamline the feedback process and improve efficiency, especially for complex manual tasks.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]