[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124056-en":3,"doc-seo-124056-105":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},124056,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Approaches Towards Cybersickness Prediction - An Updated Systematic Review","This thesis presents an updated systematic review of machine learning approaches for predicting cybersickness. It frames cybersickness prediction alongside detection, summarizes prior research, and defines research questions guiding the review. The methods section details search terms, inclusion of studies, and the organization of results by subject demographics, immersion and workload factors, and the machine learning pipeline. Discussion addresses validity of papers, evolution of feature extraction toward deep learning, real-time prediction using biosignals, data labeling, and directions for future work.","Machine learning approaches towards cybersickness prediction: An updated systematic review  \nby  \nNikoo Javadpour  \nA thesis submitted to the graduate faculty  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nMajor: Industrial Engineering & Human Computer Interaction  \nProgram of Study Committee:  \nStephen B. Gilbert, Major Professor  \nMichael C. Dorneich  \nCody Fleming  \nThe student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred.  \nIowa State University  \nAmes, Iowa  \n2024  \nCopyright © Nikoo Javadpour, 2024. All rights reserved.  \nDEDICATION  \nTo my incredible parents,  \nMom and Dad, your unyielding support has been my guiding star. This journey would have been impossible without your sacrifices. This thesis is not just a culmination of my hard work, but a testament to your dedication and love.  \nWith deepest gratitude and affection,  \nNikoo  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES ....................................................................................................................... iv  \n[LIST OF TABLES ......................................................................................................................... vi](LIST OF TABLES ......................................................................................................................... vi)  \n[ACKNOWLEDGMENTS ...........................................................................................................](ACKNOWLEDGMENTS ...........................................................................................................). vii  \nABSTRACT................................................................................................................................. viii  \nMACHINE LEARNING APPROACHES TOWARDS CYBERSICKNESS PREDICTION: AN UPDATED SYSTEMATIC REVIEW ........................................................................................ 1  \nIntroduction ................................................................................................................................. 1  \nMachine Learning and Cybersickness .................................................................................... 4  \nPrediction vs. Detection .......................................................................................................... 6  \nPrevious Cybersickness Research ........................................................................................... 7  \nResearch Questions ............................................................................................................... 13  \nStructure of Thesis ................................................................................................................ 13  \nMethods .................................................................................................................................... 14  \nSearch Terms ......................................................................................................................... 14  \nResults....................................................................................................................................... 16  \nSubject Demographics (Table 2. Subject Demographics) .................................................... 16  \nImmersion Type, Workload, and Position (Table 3. Content Information) .......................... 22  \nMachine Learning (Table 4. Machine Learning Model) ....................................................... 28  \nPreprocessing (Table 5. Preprocessing) ................................................................................ 43  \nFeatures (Table 6. Features) .................................................................................................. 49  \nDiscu","cbCaieYlGkMdpgld","https://ap.wps.com/l/cbCaieYlGkMdpgld","pdf",3123600,1,101,"English","en",105,"# Introduction\n## Machine Learning and Cybersickness\n## Prediction vs. Detection\n## Previous Cybersickness Research\n## Research Questions\n# Methods\n## Search Terms\n# Results\n## Subject Demographics\n## Immersion Type, Workload, and Position\n## Machine Learning Models\n## Preprocessing\n## Features\n# Discussion\n## Validity of Research Papers\n## Evolution of Feature Extraction Methods\n## Real-Time Detection and Prediction\n## Advances in Biosignal Analysis\n## Value of HMD Devices Alone\n## Workload and Its Effects\n## Data Labelling\n## Ideal Study\n## Future Work\n# Conclusion\n# References","[{\"question\":\"What is the focus of this updated systematic review?\",\"answer\":\"The review focuses on machine learning approaches for predicting cybersickness, organizing evidence across study factors and the machine learning pipeline.\"},{\"question\":\"How does the thesis differentiate prediction from detection?\",\"answer\":\"It explicitly discusses the distinction between predicting cybersickness outcomes and detecting symptoms, treating them as related but different tasks in the review framing.\"},{\"question\":\"What key areas are analyzed in the results?\",\"answer\":\"Results cover subject demographics, immersion type, workload and position, preprocessing steps, extracted features, and the specific machine learning models used.\"}]","Machine Learning Approaches Towards Cybersickness Prediction - 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