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.
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
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.
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)
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.
Biomechanical Assessment and Simulation
(Third Party Funds Single)Term: 1. August 2019 - 1. August 2022
Funding source: Industrie
The goal of this project is to develop data-based and knowledge-based methods for accurate analysis and simulation of human motion, focused on gait. Movement simulations are created by solving trajectory optimization problems, using an objective related to energy, a musculoskeletal model to model the body and muscle dynamics, and constraints to define the movement task. We use data-based approaches to improve musculoskeletal models and simulation accuracy. With our research, we aim to better understand human motion, and thereby improve design of wearables, such as prostheses, exoskeletons, and running shoes.
- Gambietz, M., Nitschke, M., Miehling, J., & Koelewijn, A. (2022). What should a metabolic energy model look like? Sensitivity of metabolic energy model parameters during gait. Poster presentation at 9th World Congress of Biomechanics 2022 Taipei, Taipei, TW.
- Koelewijn, A., & Selinger, J.C. (2022). Predictive Gait Simulations of Human Energy Optimization. In Juan C. Moreno, Jawad Masood, Urs Schneider, Christophe Maufroy, Jose L. Pons (Eds.), Wearable Robotics: Challenges and Trends. (pp. 377-381). Cham: Springer Science and Business Media Deutschland GmbH.
- Koelewijn, A., & Van Den Bogert, A.J. (2022). Antagonistic co-contraction can minimize muscular effort in systems with uncertainty. PeerJ, 10. https://dx.doi.org/10.7717/peerj.13085
- Nitschke, M., Mayer, M., Dorschky, E., & Koelewijn, A. (2022). How many sensors are enough? Trajectory optimization using sparse inertial sensor sets. Paper presentation at 9th World Congress of Biomechanics 2022 Taipei, Taipei, TW.
- Koelewijn, A., Nitschke, M., & van den Bogert, A.J. (2021, July). A Predictive Simulation Study into the Effect of Below-Knee Prosthesis Alignment on Metabolic Cost. Poster presentation at XXVIII Congress of the International Society of Biomechanics (ISB), Online.
- Nitschke, M., Dorschky, E., Eskofier, B., Koelewijn, A., & van den Bogert, A.J. (2021, July). Trajectory Optimization of a 3D Musculoskeletal Model with Inertial Sensors. Paper presentation at XXVIII Congress of the International Society of Biomechanics (ISB), Online.
- Nitschke, M., Luckfiel, T., Schlarb, H., Dorschky, E., & Koelewijn, A. (2021, July). Prediction of the effect of midsole stiffness and energy return using trajectory optimisation. Paper presentation at 15th biennial Footwear Biomechanics Symposium, Online.
- Schleicher, R., Nitschke, M., Martschinke, J., Stamminger, M., Eskofier, B., Klucken, J., & Koelewijn, A. (2021). BASH: Biomechanical Animated Skinned Human for Visualization of Kinematics and Muscle Activity. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP (pp. 25-36). Online.
- Dorschky, E., Nitschke, M., Martindale, C., van den Bogert, A.J., Koelewijn, A., & Eskofier, B. (2020). CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data. Frontiers in Bioengineering and Biotechnology, 8(604), 1-14. https://dx.doi.org/10.3389/fbioe.2020.00604
- Koelewijn, A., & Ijspeert, A. (2020). Exploring the Contribution of Proprioceptive Reflexes to Balance Control in Perturbed Standing. Frontiers in Bioengineering and Biotechnology, 8. https://dx.doi.org/10.3389/fbioe.2020.00866
- Koelewijn, A., & van den Bogert, A.J. (2020). A solution method for predictive simulations in a stochastic environment. Journal of Biomechanics. https://dx.doi.org/10.1016/j.jbiomech.2020.109759
- Nitschke, M., Dorschky, E., Heinrich, D., Schlarb, H., Eskofier, B., Koelewijn, A., & van den Bogert, A.J. (2020). Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics. Scientific Reports. https://dx.doi.org/10.1038/s41598-020-73856-w