In diesem Semester wird die im WiSe 2022/23 erfolgreich gestartete Vortragsreihe 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Donnerstag ab 16:00 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes 威尼斯赌博游戏_威尼斯赌博app-【官网】ical Information Sciences.

Clinical gait analysis is a central approach for assessing human gait function, enabling accurate diagnoses and effective treatment plans. Instrumented 3D gait analysis, the gold standard, provides detailed biomechanical insights but remains laboratory-bound and time-intensive. Artificial Intelligence (AI), specifically machine learning, has demonstrated significant potential for advancing gait analysis. However, the implementation of machine learning models in clinical practice is hindered by the "black-box" nature of state-of-the-art models, particularly those based on deep learning algorithms.?In response, the field of eXplainable AI (XAI) has received increasing attention in recent years. XAI methods provide insights into how machine learning models operate and how they make their predictions. This talk presents research applying XAI methods in gait analysis, demonstrating their capacity to enhance model transparency, improve evaluation frameworks, and uncover novel biomechanical insights.

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