Prof. Dr. Anne Koelewijn

Chair of Machine Learning and Data Analytics Lab

It is my goal to improve movement for people, e.g. those with a disability or athlete. To do so, I aim to better understand human motion, and design better devices, such as prostheses, exoskeletons, and running shoes, as well as prevent injuries, such as knee osteoarthritis. I focus on wearable technologies and the combination of physics-based models with machine learning methods.

Research projects

Personalized musculoskeletal models and gait simulations: using imaging techniques such as diffusion tensor imaging (DTI), as well as using machine learning methods, we aim to personalize musculoskeletal models and gait simulations, such that we can per

  • Bridging the gap in ACL injury prevention with FAME: Field-based Athlete Motion Evaluation and simulation

    (Third Party Funds Single)

    Term: since 15. January 2024
    Funding source: Deutsche Forschungsgemeinschaft (DFG)
  • Maschinelle Lernverfahren zur Personalisierung muskuloskelettaler Menschmodelle, Bewegungsanalyse

    (Third Party Funds Group – Sub project)

    Overall project: Empathokinästhetische Sensorik - Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung
    Term: 1. July 2021 - 30. June 2025
    Funding source: DFG / Sonderforschungsbereich (SFB)
    URL: https://www.empkins.de/
    The extent to which a neural network can be used to effectively personalise gait simulations using motion data is explored. We first investigate the influence of body parameters on gait simulation. An initial version of the personalisation is trained with simulated motion data, since ground truth data is known for this purpose. We then explore gradient-free methods to fit the network for experimental motion data. The resulting network is validated with magnetic resonance imaging, electromyography and intra-body variables.

2025

2024

2023

2022

2021

2020

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