Biomechanical Motion Analysis and Creation
The Biomechanical motion analysis and creation (BioMAC) group aims to develop 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. Experimental studies are used to capture human motion for tracking and as reference. With our research, we 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. Thereby, we focus on wearables and the combination of physics-based models with machine learning methods.
Group Head
Group Members
Current Students
- Annika Wünsch
Explaining influential differences in human gait datasets on machine learning tasks - Daniel Janischowsky
Predictive simulations of gait with ankle exoskeleton that alters energetics - Prajjwal Nag
Smartphone-Based Human Model Personalisation
Presentations and Videos
To the videos
Demo
This demo shows how the developed methods can be used to reconstruct measured movement but also to predict movements in response to environmental changes:
Projects
- Applications of Biomechanical Simulations
- Fundamentals of Biomechanical Simulations
- Personalization of Muscoskeletal Models
- Digital Twin of the Musculoskeletal System
- Machine Learning for Personalisation of Biomechanical Movement Simulations (C01)
- Machine Learning for Neuromusculoskeletal Modelling (C03)
- Individual Performance Prediction Using Musculoskeletal Modeling
- Biomechanical Assessment of Big Wave Surfing
- TEF-Health
