Project: musculoskeletal Modeling

Master project — 2023

The project focused on leveraging musculoskeletal modelling, employing advanced tools like OpenSim, to enhance movement measurements and motion prediction. We learned to create detailed musculoskeletal models, apply inverse dynamics, optimisation and forward dynamics for different analysis goals. Our research delved into cycling power transfer efficiency, deepening our modelling expertise and research proposal skills.

Musculoskeletal modelling, OpenSim software, inverse dynamics, forward dynamics, movement prediction, research skills, Matlab, data analysis

Throughout this project, we learned how to build and apply musculoskeletal models to improve movement measurements and predict behaviour, thereby enhancing our understanding of human movement beyond observational analysis alone. Employing cutting-edge modelling software, OpenSim, allowed us to develop and analyse mathematical models of the human musculoskeletal system. The building blocks of these musculoskeletal models are the bones and joints, complemented by the integration of muscles, tendons and their dynamics. Furthermore, we gained knowledge into the use of inverse dynamics and optimisation techniques to extract unmeasured quantities, such as joint kinematics, work, power, and more. Forward dynamics techniques were applied to simulate movements and perform predictive analyses of motion. This knowledge was applied to our original research.

Within our own research, my project partner and I explored the power transfer efficiency between the human body and a bicycle, disregarding aerodynamic drag. Our investigation into this efficiency stems from the assumption that a more upright cycling posture can reduce strain on the back and enhance awareness of the surroundings. To explore the difference in power transfer efficiency at different back angles, a musculoskeletal model of the lower extremities and BMX bicycle was provided. The model, as shown in the accompanying images, was fine-tuned to accommodate the situation of interest. The final model was utilised to generate motion data corresponding to cycling movements, followed by a comprehensive forward dynamics analysis. Subsequently, Matlab was used to generate significant plots to interpret the collected data, as depicted in the images.

This project not only broadened my experience with musculoskeletal modelling but also deepened my understanding of the difference between inverse and forward dynamics. Furthermore, we learned how to formulate compelling and professionally meaningful research questions, and combining these into a well-structured research proposal.

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