Human locomotion is influenced by various factors such pain, fatigue, congenital/neuromuscular disorders. Although clarifying the causes and effects of human gait has long been an important research topic in biomechanics, biomedical engineering, and clinical gait analysis, the causality has been unraveled only partially yet. It is still very challenging to predict how the causes, such as muscle weakness and nerve damage, lead to gait disturbance. Recent developments in artificial intelligence and machine learning technology have led to innovative changes in many research areas. These changes are expected to bring about important improvements in medical field as well. In this talk, we are going to discuss how to leverage artificial intelligence and machine learning technologies for the analysis of the pathologic gait of patients and the prediction of the outcome of gait correcting surgery using patient-specific musculoskeletal models.

Jehee Lee is a professor in the Department of Computer Science and Engineering at Seoul National University. His research areas include computer graphics, animation, biomechanics, and robotics. He is interested in developing new ways of understanding, representing, planning and simulating human and animal movements. This involves full-body motion analysis and synthesis, biped control and simulation, clinical gait analysis, motion capture, motion planning, data-driven and physically based techniques, and control learning. He co-chaired ACM/EG Symposium on Computer Animation (SCA) in 2012 and ACM/SIGGRAPH Conference on Motion, Interaction and Games (MIG) in 2018. He is currently an associate editor of IEEE Transactions on Visualization and Computer Graphics. He also served on numerous program committees, including ACM SIGGRAPH, ACM SIGGRAPH Asia, Pacific Graphics, CGI, and CASA. He is leading the SNU Movement Research Laboratory.