[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121405-en":3,"doc-seo-121405-105":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},121405,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Impact of Time Discrepancy on Machine Learning Models for Human Activity Recognition - Diplomarbeit","The thesis investigates how time discrepancies affect machine learning models designed for human activity recognition. It analyzes the consequences of temporal misalignment on model performance, robustness, and evaluation reliability, focusing on how variations in timing can distort observed sensor patterns and learned temporal relationships. It further documents experimental setup and computational workflow, including the use of high-performance resources to obtain and validate results. The work supports conclusions about the importance of handling time differences during data preparation and model development.","Impact of Time Discrepancy on Machine Learning Models for Human Activity Recognition  \nDIPLOMARBEIT  \nzur Er langung des akademischen Grades  \nDiplom-Ingenieur  \nim Rahmen des Studiums  \nMedia and Human-Centered Computing  \neingereicht von  \nPatrick Trollmann, BSc  \nMatrikelnummer 11907096  \nan der Fakultät für Informatik der Technischen Universität Wien  \nBetreuung: [Univ.Prof. Dipl.-Inf. Dr.sc.techn. Florian Michahelles](Univ.Prof. Dipl.-Inf. Dr.sc.techn. Florian Michahelles)  \nUniv.Ass. Dr.-Ing. Florian Wolling, MSc Mitwirkung: Projektass. Dipl.-Ing. David Kostolani, BSc  \nWien, 15. März 2025      \nPatrick Trollmann Florian Michahelles  \nTechnische Universität Wien  \nA-1040 Wien  Karlsplatz 13  Tel. +43-1-58801-0  [www.tuwien.at](www.tuwien.at)  \nImpact of Time Discrepancy on Machine Learning Models for Human Activity Recognition  \nDIPLOMA THESIS  \nsubmitted in partial fulfillment of the requirements for the degree of  \nDiplom-Ingenieur  \nin  \nMedia and Human-Centered Computing  \nby  \nPatrick Trollmann, BSc  \nRegistration Number 11907096  \nto the Faculty of Informatics at the TU Wien  \nAdvisor: [Univ.Prof. Dipl.-Inf. Dr.sc.techn. Florian Michahelles](Univ.Prof. Dipl.-Inf. Dr.sc.techn. Florian Michahelles)  \nUniv.Ass. Dr.-Ing. Florian Wolling, MSc Assistance: Projektass. Dipl.-Ing. David Kostolani, BSc  \nVienna, March 15, 2025      \nPatrick Trollmann Florian Michahelles  \nTechnische Universität Wien  \nA-1040 Wien  Karlsplatz 13  Tel. +43-1-58801-0  [www.tuwien.at](www.tuwien.at)  \nErklärung zur Verfassung der Arbeit  \nPatrick Trollmann, BSc  \nHiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die verwendeten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit – einschließlich Tabellen, Karten und Abbildungen –, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe.  \nIch erkläre weiters, dass ich mich generativer KI-Tools lediglich als Hilfsmittel bedienthabe und in der vorliegenden Arbeit mein gestalterischer Einfluss überwiegt. Im Anhang „Übersicht verwendeter Hilfsmittel“ habe ich alle generativen KI-Tools gelistet, die verwendet wurden, und angegeben, wo und wie sie verwendet wurden. Für Textpassagen, die ohne substantielle Änderungen übernommen wurden, habe ich jeweils die von mir formulierten Eingaben (Prompts) und die verwendete IT-Anwendung mit ihrem Produktnamen und Versionsnummer/Datum angegeben.  \nWien, 15. März 2025    \nPatrick Trollmann  \nv  \nDanksagung  \nIch möchte gerne meinen aufrichtigen Dank an folgende Personen aussprechen, ohne die die Fertigstellung meiner Diplomarbeit nicht möglich gewesen wäre.  \nFlorian  \nIch bin zutiefst dankbar für meinen Betreuer Dr.-Ing. Florian Wolling, MSc für seine kontinuierliche Hilfe, seine Führung und Ratschläge durch komplexe Themen hindurch, welcher mich durch meine gesamte Arbeit durch hinweg begleitet hat. Seine Expertise hat dazu beigetragen den richtigen Weg zu zeigen und die Qualität dieser Arbeit zu verbessern.  \nDavid  \nIch möchte außerdem Dipl.-Ing. David Kostolani, BSc danken, welcher mir wichtige Tipps und Tricks zum Thema Machine Learning gezeigt hat, und so auch dazu beigetragen hat meine Arbeit zu verbessern.  \nRita  \nEin weiterer und aufrichtiger Dank geht an meine Freundin und Partnerin, welche mich mit endloser Geduld unterstützt hat und mich in den vielen durchgearbeiteten Nächten stets motiviert hat.  \nMeinen Eltern  \nNicht zuletzt bin ich auch meinen Eltern zu einem großen Dank verpflichtet. Ihr unerlässlicher Glaube an meine Fähigkeiten und ihre finanzielle Unterstützung waren Säulen, auf denen ich meine akademischen Leistungen aufbauen konnte.  \nVSC  \nDie präsentierten Rechenergebnisse wurden [zum Teil] am Vienna Scientific Cluster (VSC) erzielt.  \nDiese Diplomarbeit ist das Ergebnis von gemeinsamen Anstrengungen und der Unterstützung von wahnsinnig wunderbaren Menschen, und ich bin ihnen ","cbCaih4fTKXKGonu","https://ap.wps.com/l/cbCaih4fTKXKGonu","pdf",11939445,1,132,"English","en",105,"# Acknowledgements\n## Erklärung zur Verfassung der Arbeit\n## Danksagung\n## Acknowledgements","[{\"question\":\"What is the main topic of the thesis?\",\"answer\":\"The thesis focuses on how time discrepancies influence machine learning models for human activity recognition.\"},{\"question\":\"Why are time discrepancies important for human activity recognition?\",\"answer\":\"Temporal misalignment can change sensor patterns and interfere with the temporal dependencies models learn, affecting performance and evaluation.\"},{\"question\":\"What resources were used to obtain the computational results?\",\"answer\":\"Some of the computational results were achieved using the Vienna Scientific Cluster (VSC).\"}]","Impact of Time Discrepancy on Machine Learning Models for Human Activity Recognition - 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