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 2024Funding source: Deutsche Forschungsgemeinschaft (DFG)
Biomechanical Assessment of Big Wave Surfing
(Third Party Funds Single)
Term: 1. June 2022 - 31. May 2025Funding 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 2022Funding source: Siemens AG
Individual Performance Prediction Using Musculoskeletal Modeling
(Third Party Funds Single)
Term: 1. February 2022 - 31. January 2025Funding 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.
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.
Recent publications
2024
Gambietz, M., Dröge, A., Schüßler, C., Stahlke, M., Wirth, V., Miehling, J., & Koelewijn, A. (2024). Unobtrusive Gait Reconstructions using Radar-based Optimal Control Simulations . In Proceedings of the Asilomar Conference on Signals, Systems, and Computers .
Krauß, D., Engel, L., Ott, T., Bräunig, J., Richer, R., Gambietz, M.,... Vossiek, M. (2024). A Review and Tutorial on Machine Learning- Enabled Radar-Based Biomedical Monitoring . IEEE Open Journal of Engineering in Medicine and Biology , 1-22. https://doi.org/10.1109/OJEMB.2024.3397208
Miehling, J., Choisne, J., & Koelewijn, A. (2024). Editorial: Human digital twins for medical and product engineering . Frontiers in Bioengineering and Biotechnology , 12 . https://doi.org/10.3389/fbioe.2024.1489975
Mohr, M., Federolf, P., Heinrich, D., Nitschke, M., Raschner, C., Scharbert, J., & Koelewijn, A. (2024). An 8-week injury prevention exercise program combined with change-of-direction technique training limits movement patterns associated with anterior cruciate ligament injury risk . Scientific Reports , 14 (1), 3115-. https://doi.org/10.1038/s41598-024-53640-w
Mohr, M., Federolf, P., Heinrich, D., Nitschke, M., Raschner, C., Scharbert, J., & Koelewijn, A. (2024). Correction to: An 8-week injury prevention exercise program combined with change-of-direction technique training limits movement patterns associated with anterior cruciate ligament injury risk (Scientific Reports, (2024), 14, 1, (3115), 10.1038/s41598-024-53640-w) . Scientific Reports , 14 (1). https://doi.org/10.1038/s41598-024-56031-3
Nguyen, D.T., Zieger, D., Gambietz, M., Koelewijn, A., Stamminger, M., & Kaup, A. (2024). Multiresolution point cloud compression for real-time visualization and streaming of large 3D datasets . In Proceedings of the Asilomar Conferense on Signals, Systems, and Computers .
Nitschke, M., Dorschky, E., Leyendecker, S., Eskofier, B., & Koelewijn, A. (2024). Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations . Frontiers in Bioengineering and Biotechnology , 12 . https://doi.org/10.3389/fbioe.2024.1285845
Schlechtweg, N., Brückner, S., Gambietz, M., Koelewijn, A., & Vossiek, M. (2024). Time-Synchronized Joint Communication and Precise Wireless Localization of Multiple On-Body Sensor Nodes for Human Gait and Movement Measurement . In Proceedings of the Asilomar Conference on Signals, Systems, and Computers .
Shanbhag, J., Fleischmann, S., Gaßner, H., Winkler, J., Eskofier, B., Koelewijn, A.,... Miehling, J. (2024). Modelling postural control of upright standing during translational perturbations .
Shanbhag, J., Fleischmann, S., Wechsler, I., Gaßner, H., Winkler, J., Eskofier, B.,... Miehling, J. (2024). A sensorimotor enhanced neuromusculoskeletal model for simulating postural control of upright standing . Frontiers in Neuroscience , 18 . https://doi.org/10.3389/fnins.2024.1393749
Voß, M., Koelewijn, A., & Beckerle, P. (2024). Intuitive and versatile bionic legs: a perspective on volitional control . Frontiers in Neurorobotics , 18 . https://doi.org/10.3389/fnbot.2024.1410760
Wechsler, I., Wartzack, S., Koelewijn, A., & Miehling, J. (2024). IMU-based direct analytical joint center identification method for OpenSim – A proof of concept . In Proceedings of the 29th Congress of the European Society of Biomechanics . Edinburgh, Scotland.
Wechsler, I., Wartzack, S., Koelewijn, A., & Miehling, J. (2024, July). IMU-based identification method for joint axes in OpenSim – a proof of concept .
Wechsler, I., Wolf, A., Shanbhag, J., Leyendecker, S., Eskofier, B., Koelewijn, A.,... Miehling, J. (2024). Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results—a systematic review . Frontiers in Bioengineering and Biotechnology , 12 . https://doi.org/10.3389/fbioe.2024.1386874
2023
Bachhuber, S., Lehmann, D., Dorschky, E., Koelewijn, A., Seel, T., & Weygers, I. (2023). Plug-and-Play Sparse Inertial Motion Tracking With Sim-to-Real Transfer . IEEE Sensors Letters , 7 (10). https://doi.org/10.1109/LSENS.2023.3307122
Dodge, B., Bennett, H.J., Kravchenko, O., Jamora, V., Parente, M., Jorge, R.N.,... Audette, M. (2023). Towards a Patient-Specific Obstetric Simulator Through OpenSim Musculoskeletal Modelling . In Proceedings of the 2023 Annual Modeling and Simulation Conference (ANNSIM) . Ontario, Canada.
Dorschky, E., Camomilla, V., Davis, J., Federolf, P., Reenalda, J., & Koelewijn, A. (2023). Perspective on “in the wild” movement analysis using machine learning . Human Movement Science , 87 . https://doi.org/10.1016/j.humov.2022.103042
Fleischmann, S., Shanbhag, J., Miehling, J., Wartzack, S., Leyendecker, S., Koelewijn, A., & Eskofier, B. (2023). Time vs. Space: Comparing gait cycle normalization methods and their effect on foot placement control .
Gambietz, M., Nitschke, M., Miehling, J., & Koelewijn, A. (2023). Contributing Components of Metabolic Energy Models to Metabolic Cost Estimations in Gait . IEEE Transactions on Biomedical Engineering , 1-9. https://doi.org/10.1109/TBME.2023.3331271
Koelewijn, A., Nitschke, M., & Leyendecker, S. (2023). “In the Wild" Movement Analysis of Arbitrary Motions .
Krapovickas, V., Vera, R.B., Farina, M.E., Fernandez Piana, L.R., & Koelewijn, A. (2023). A new vertebrate ichnological association sheds light on the small metatherian record of the Middle Miocene in South America . Journal of South American Earth Sciences , 129 . https://dx.doi.org/10.1016/j.jsames.2023.104529
Nitschke, M., Marzilger, R., Leyendecker, S., Eskofier, B., & Koelewijn, A. (2023). Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data . PeerJ . https://doi.org/10.7717/peerj.14852
Shanbhag, J., Fleischmann, S., Eskofier, B., Koelewijn, A., Wartzack, S., & Miehling, J. (2023). Towards postural control simulation using a sensorimotor enhanced musculoskeletal human model .
Shanbhag, J., Wolf, A., Wechsler, I., Fleischmann, S., Winkler, J., Leyendecker, S.,... Miehling, J. (2023). Methods for integrating postural control into biomechanical human simulations: a systematic review . Journal of neuroEngineering and rehabilitation , 20 (1). https://doi.org/10.1186/s12984-023-01235-3
Wang, C., Nitschke, M., Stefanyshyn, D., Wannop, J.W., Luckfiel, T., Schlarb, H., & Koelewijn, A. (2023). Prediction of the effect of stack height on running biomechanics using optimal control simulation . In Footwear Science . Osaka, Japan.
Wechsler, I., Koelewijn, A., Wartzack, S., & Miehling, J. (2023, July). Towards individualized biomechanical models in multiple domains .
2022
Egle, F., Kluge, F., Schöne, D., Becker, L., & Koelewijn, A. (2022). Development of an Inertial Sensor-Based Exergame for Combined Cognitive and Physical Training . In IEEE (Eds.), 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) . Ioannina, GR.
Fleischmann, S., Nitschke, M., Marzilger, R., & Koelewijn, A. (2022). Can we combine data sets? Feature extraction and clustering motion capture data .
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 .
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., & Selinger, J.C. (2022). Predictive Simulations to Replicate Human Gait Adaptations and Energetics with Exoskeletons . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 30 , 1931 - 1940. https://doi.org/10.1109/TNSRE.2022.3189038
Koelewijn, A., & Selinger, J.C. (2022). Predictive Simulations to Replicate Human Gait Adaptations and Energetics With Exoskeletons . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 30 , 1931-1940. https://doi.org/10.1109/TNSRE.2022.3189038
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., Marzilger, R., & Koelewijn, A. (2022). 3D full-body optimal control simulations with change of direction directly driven by motion capture data .
Nitschke, M., Marzilger, R., Leyendecker, S., Eskofier, B., & Koelewijn, A. (2022). Optical motion capturing of change of direction motions reconstructed with inverse kinematics and dynamics and optimal control simulation .
Nitschke, M., Mayer, M., Dorschky, E., & Koelewijn, A. (2022). How many sensors are enough? Trajectory optimization using sparse inertial sensor sets .
2021
Dorschky, E., Nitschke, M., van den Bogert, A.J., Koelewijn, A., & Eskofier, B. (2021). Machine Learning from Biomechanical SimulaIons for an "in the Wild" Movement Analysis .
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 .
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 .
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 .
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.
2020
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://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://doi.org/10.1038/s41598-020-73856-w
Related Research Fields
Contact:
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)
Funding source: Deutsche Forschungsgemeinschaft (DFG)
Biomechanical Assessment of Big Wave Surfing
(Third Party Funds Single)
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:
Biomechanical Assessment of Big Wave Surfing
(Third Party Funds Single)
Funding source: Siemens AG
Individual Performance Prediction Using Musculoskeletal Modeling
(Third Party Funds Single)
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.
Maschinelle Lernverfahren zur Personalisierung muskuloskelettaler Menschmodelle, Bewegungsanalyse
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
2024
2023
2022
2021
2020
Related Research Fields
Contact: