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

Current projects

  • 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)
  • Biomechanical Assessment of Big Wave Surfing

    (Third Party Funds Single)

    Term: 1. June 2022 - 31. May 2025
    Funding source: Siemens AG

    The goal of this project is to develop experimental approaches and simulation methods for biomechanical assessment of big wave surfing. This goal will be addressed in collaboration with Sebastian Steudtner and Siemens Healthineers. The methods include, but are not limited to, biomechanical movement analysis, musculoskeletal simulation, and sensor fusion.

    The focus of the research activities will be centered on:

    • Development of a measurement approach for biomechanical assessment of big wave surfing
    • Development of efficient and accurate data processing combining inputs from several sensor systems
    • Design of a biomechanical simulation model that reflects the situation during surfing
    • Analysis of biomechanical measurements and simulation outcomes to provice advice for big wave surfer to improve performance. 
  • Biomechanical Assessment of Big Wave Surfing

    (Third Party Funds Single)

    Term: since 1. June 2022
    Funding source: Siemens AG
  • Individual Performance Prediction Using Musculoskeletal Modeling

    (Third Party Funds Single)

    Term: 1. February 2022 - 31. January 2025
    Funding source: Industrie

    Biomechanical modeling and simulation are performed to analyze and understand human motion and performance. One objective is to reconstruct human motion from measurement data e.g. to assess the individual performance of athletes and customers. Another objective is to synthesize realistic human motion to study human-production interaction. The reconstruction (a) and synthesis of human motion (b) will be addressed in this  research position. New algorithms using biomechanical simulation of musculoskeletal models will be developed to enable innovative applications and services for Adidas. Moreover, predictive biomechanical simulation will be combined with wearable sensor technology to build a product recommendation application.

  • Digital Twin of the Musculoskeletal System

    (Third Party Funds Group – Sub project)

    Overall project: dhip campus-bavarian aim
    Term: 1. September 2021 - 31. August 2024
    Funding source: Industrie

    Musculoskeletal (MSK) models represent the dynamics of the human body and can output many different variables i.e. joint angles, joint moments and muscle force. Personalised movement predictions provide accurate outcome variables than a generic prediction. Therefore, we would like to develop a digital twin of the MSK system, which can then be used for personalised movement predictions. Image-based personalisation is the state-of-the-art. Anthropometric variables, such as bone geometry and muscle attachment points can be derived from magnetic resonance imaging (MRI). Muscle parameters require diffusion tensor imaging (DTI) to visualise the alignment of fibres, which is important for the derivation of the muscle size as well as the fibre length. The goal of this project is to develop a personalised digital twin of the MSK system using DTI measurements, and investigate if such a digital twin can improve accuracy of movement predictions.  The aim is to also investigate to what extent image processing can be automated. Furthermore, identification of groups using the personalised models, e.g. to detect MSK diseases, such as rheumatoid arthritis will be investigated. 

  • 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)
    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.

Recent publications






Related Research Fields