Machine Learning for Movement Science
Using various machine learning tecnhiques, the ML4MS lab investigates diverse movement science topics, from spinal cord modeling with neural networks to applied research in pose estimation and golf performance
Meet the ML4MS Team
Myriam de Graaf
© Kirandeep Kaur Myriam de Graaf is a Ph.D. candidate at the University of Münster, specializing in machine learning applications for human movement science. She completed her bachelor's and research master's degrees in Human Movement Science at the Vrije Universiteit (VU) Amsterdam before joining the University of Münster in October 2019 to pursue her Ph.D.
Currently, Myriam's research focuses on using artificial neural networks to model spinal cord function, aiming to elucidate motor control mechanisms during locomotion. Additionally, she collaborates on studies investigating joint contact forces and metabolic rates in locomotion, as well as applying machine learning techniques to golf performance and motion capture analysis. Beyond her research, Myriam has developed two courses designed to introduce movement science students to machine learning as part of the InterKI project. These courses are still offered and can be accessed via learnweb.
Yu Yuan (Berry) Lee
© Berry (Yu Yuan) Lee Yu Yuan (Berry) Lee is a Ph.D. candidate at the University of Münster, where he has been conducting research since 2023. His work focuses on biomechanical simulations and the application of AI in biomechanics. With a B.Sc. in Biomedical Science and an M.Sc. in Sports, Exercise & Human Performance from the University of Münster, Yu Yuan has a solid academic foundation and diverse expertise
His research spans several projects, including improving video-based motion capture with deep learning, predicting fall risk using machine learning, developing smart gloves to analyse object manipulation, and simulating bike crashes to study airbag protection.