[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128063-en":3,"doc-seo-128063-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128063,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Human Gait Analysis - Machine Learning-Based Classification of Gait Disorders","The dissertation addresses clinical gait analysis and its reliance on complex, multivariate, high-dimensional time series derived from kinematic and kinetic measurements. It reviews limitations of existing machine learning approaches, especially those caused by small datasets and simplified classification settings with only a few classes. The work develops and evaluates traditional machine learning and deep learning methods tailored to gait data, aiming to improve diagnostic efficiency and decision support. It further proposes explainable ML approaches to tackle complex multi-class classification tasks and to enable interpretable results for clinicians and researchers.","Human Gait Analysis Machine Learning-Based Classification of Gait  \nDisorders  \nDISSERTATION  \nzur Erlangung des akademischen Grades  \nDoktor der Technischen Wissenschaften  \neingereicht von  \nDipl.-Ing. Djordje Slijepˇcevi´c, BSc  \nMat rikelnummer 00925240  \nan der Fakultät für Informatik der Technischen Universität Wien  \nBetreuung: Univ.-Prof. Dipl.-Ing. Dr.techn. Christian Breiteneder  \nZweitbetreuung: FH-Prof. Priv.-Doz. Dipl.-Ing. Mag. Dr. Matthias Zeppelzauer  \nDiese Dissertation haben begutachtet:  \nNeil Cronin Morgan Sangeux  \nWien, 21. Mai 2024    \nDjordje Slijepˇcevi´c  \nTechnische Universität Wien A-1040 Wien  Karlsplatz 13  Tel. +43-1-58801-0  [www.tuwien.at](www.tuwien.at)  \nHuman Gait Analysis Machine Learning-Based Classification of Gait  \nDisorders  \nDISSERTATION  \nsubmitted in partial fulfillment of the requirements for the degree of  \nDoktor der Technischen Wissenschaften  \nby  \nDipl.-Ing. Djordje Slijepˇcevi´c, BSc  \nRegistration Number 00925240  \nto the Faculty of Informatics at the TU Wien  \nAdvisor: Univ.-Prof. Dipl.-Ing. Dr.techn. Christian Breiteneder  \nSecond advisor: FH-Prof. Priv.-Doz. Dipl.-Ing. Mag. Dr. Matthias Zeppelzauer  \nThe dissertation has been reviewed by:  \nNeil Cronin Morgan Sangeux  \nVienna, 21st May, 2024    \nDjordje Slijepˇcevi´c  \nTechnische Universität Wien A-1040 Wien  Karlsplatz 13  Tel. +43-1-58801-0  [www.tuwien.at](www.tuwien.at)  \nErklärung zur Verfassung der Arbeit  \nDipl.-Ing. Djordje Slijepˇcevi´c , 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.  \nWien, 21. Mai 2024    \nDjordje Slijepˇcevi´c  \nv  \nAcknowledgements  \nI am deeply grateful for the guidance, support, and expertise of my first supervisor, Prof. Christian Breiteneder, whose insights and direction were invaluable throughout this journey. His profound knowledge has been a driving force and inspiration behind this dissertation.  \nA heartfelt thanks to Matthias Zeppelzauer, my second supervisor and daily mentor atthe university. Matthias, your involvement in the day-to-day aspects of my academic work and your mentorship have been fundamental in shaping the quality and direction of this research.  \nMy gratitude extends to Brian Horsak, my mentor in the field of human gait analysis. Brian, your leadership in the gait analysis projects at the university and your insightful feedback have been essential in my understanding and exploration of this complex subject.  \nA special thanks to Fabian Horst for the enriching collaboration over the last years. Your moral and content-wise support, especially during the crucial stages of this thesis, have been of immense value.  \nI would like to express my appreciation to all the co-authors of the publications related to this thesis. Your contributions and collaborative spirit have been essential in achieving the quality of the presented work.  \nA special thanks to Jürgen Pannosch and Lukas Daniel Klausner for their meticulous proofreading and feedback.  \nTo my parents, Nataša and Radomir, and my brother Dimitrije, your unwavering support and belief in me have been the foundation for this achievement. This journey would not have been possible without your love, encouragement, and sacrifice. Your motivating words from the Master’s thesis period, “Piši, piši ponekad dva-tri reda” have motivated me throughout this endeavor as well.  \nLast but not least, to my wife Birgit and my two children, Lora and Iva, your patience, understanding, and unending support have been my greatest strength. Balancing family life with academic endeavors is a challenge, one that would have been impossible to overcome without your constant love and support (e.g., such","cbCaimEFVISNN90U","https://ap.wps.com/l/cbCaimEFVISNN90U","pdf",11854571,4,1,163,"English","en",105,"# Kurzfassung\n## Ziel und Motivation\n## Datengrundlagen und klinische Aufgaben\n## ML-Methoden und Deep Learning\n## Einschränkungen bestehender Ansätze\n## Beitrag der Dissertation und erklärbare Modelle","[{\"question\":\"Welche Datenquellen werden in der klinischen Ganganalyse verwendet?\",\"answer\":\"Die klinische dreidimensionale Ganganalyse kombiniert kinematische Daten wie Gelenkwinkel aus optischer Bewegungserfassung und kinetische Daten wie Bodenreaktionskräfte aus Kraftmessplatten.\"},{\"question\":\"Warum werden Methoden des maschinellen Lernens in der Ganganalyse eingesetzt?\",\"answer\":\"ML soll die Effizienz der klinischen Ganganalyse erhöhen und Forschenden sowie KlinikerInnen ermöglichen, große Mengen an Gangdaten auszuwerten, um neue Erkenntnisse für die Entscheidungsfindung zu gewinnen.\"},{\"question\":\"Welche zentralen Einschränkungen bestehender ML-Ansätze werden behandelt?\",\"answer\":\"Die Arbeit nennt insbesondere kleine Datensätze und vereinfachte Aufgabenstellungen mit wenigen Klassen als häufige Limitierungen.\"}]","Human Gait Analysis - 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