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AI / Data-driven modelling in biomechanics II: Gait
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Presentations | ||||||
17:00 - 17:12
ID: 946 GAIT PHASE IDENTIFICATION BASED ON IMU READOUTS USING THREE GRADIENT-BOOSTED MODELS University of Alberta, Canada
17:12 - 17:24
ID: 954 CLASSIFYING PHYSICAL ACTIVITY LEVEL VIA KINEMATIC GAIT DATA 1Department of Research and Development, LUNEX International University, Differdange, Luxembourg; 2Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, Italy; 3Luxembourg Health & Sport Sciences Research Institute ASBL, Differdange, Luxembourg; 4Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy; 5Istituto di Ricerca e Cura a Carattere Scientifico Centro Neurolesi “Bonino Pulejo”, Messina, Italy
17:24 - 17:36
ID: 181 ASSISTING CLINICAL DECISION-MAKING BY PREDICTING TREATMENT RESPONSE FOR PAEDIATRIC MOVEMENT DISORDERS 1ETH Zürich, Switzerland; 2University of Basel, Switzerland; 3Signapore-ETH Centre, Singapore; 4University Children's Hospital Basel, Switzerland
17:36 - 17:48
ID: 721 FALL RECOVERY LIMITATIONS FOR YOUNG ADULT AND ELDERLY MODELS THROUGH COUPLED DEEP REINFORCEMENT LEARNING SIMULATIONS 1Université de Technologie de Compiègne, Alliance Sorbonne Université, CNRS, UMR 7338 Biomécanique et Bio-ingénierie, Centre de Recherche Royallieu, CS 60 319 Compiègne, France; 2Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, F-59000 Lille, France
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