personalized models
Diversity in modelling
The aim of this project is to develop an automatic pipeline to generate subject-specific musculoskeletal models from optical 3D surface scans. Optical 3D scans can be acquired within one minute and reproduce the body shape of the patient. By training a neural network, we can accurately estimate internal parameters like skeleton shape and muscle properties based on the external body shape captured by the scans. This innovative approach eliminates the reliance on medical imaging data or manual input, making our pipeline applicable for integration into clinical and sports practices.


Judith Cueto Fernandez
PhD Candidate
Caretech symposium 2023
“We are collecting a dataset of full-body MRI scans and optical 3D body scans of healthy adults.”

— Judith Cueto Fernandez
ESB conference 2024
Currently, musculoskeletal (MSK) models can be personalized by processing medical imaging data, like magnetic resonance imaging (MRI) or computerized tomography (CT), which is time-consuming and costly. In the absence of medical imaging, it is common to linearly scale (no deformations) a generic MSK model with male bone geometries to resemble an individual [1,2]. The aim of our project is to develop a pipeline to enable the creation of personalized musculoskeletal models from optical 3D body surface scans. This abstract shows preliminary data emphasizing the differences in anatomical bony landmarks, joint centres and muscle moment arms between a MSK model with personalized geometries and two linearly scaled generic models.
Finished Graduation projects
Wies’ thesis focused on an important issue in biomechanics: current open-source musculoskeletal models are based on male bone geometry, despite known sex differences. This bias raises concerns about how accurately these models represent female biomechanics, a question that Judith Cueto Fernandez is further exploring in her PhD.
In her MSc thesis, Wies van de Meerakker investigated whether sex-based differences in pelvis and femur shape can predict variations in muscle volume distribution. Using MRI-based segmentation models and key anatomical landmarks, she developed new ways to analyze these differences.
Her work is a step toward more inclusive biomechanical models and a valuable contribution to our research on gender diversity in biomechanics.
During her project, Yoni developed a generic musculoskeletal model based on a female skeleton, addressing a long-standing gap in biomechanical modeling. Historically, many widely used musculoskeletal models have been based primarily on male anatomy, which can limit accuracy when analyzing female participant data.
Yoni systematically compared her newly developed female-based model to the existing male-based standard. Her work represents a significant step toward more inclusive and precise biomechanical analysis.
This will now support the next phase of research by our PhD candidate, Judith Cueto Fernandez, who will continue building on these findings.

In her work, Iris van Kan explored the intersection of medical imaging, anatomical dissection, and biomechanics. By directly comparing imaging data with dissected specimens, she investigated how tissue fixation influences muscle morphology. Her findings revealed important variability in post-fixation muscle swelling between different muscles, providing new insights into the reliability and interpretation of dissection-based muscle volume estimations in #biomechanics research.
In addition, Iris examined the potential of handheld 3D scanning technology to improve how muscle morphology and architecture are documented during dissection. Her work highlights how digital tools can enhance precision, reproducibility, and transparency in anatomical research.
The muscle force–length relationship is a fundamental parameter in musculoskeletal simulations. Although ex vivo studies indicate that meaningful demographic differences may exist, there is currently insufficient data to investigate these variations at scale.
In his MSc thesis, Tieme developed the first prototype of an experimental setup designed to measure this relationship across a larger and more diverse population—an essential step toward closing this data gap.
Research does not always unfold exactly as planned, but unexpected challenges often yield the most valuable lessons. Through this project, we gained critical insights into designing and optimizing the experimental pipeline for large-scale data collection, laying the groundwork for future studies.





MSc students working on this project

Lieke Vannisselroy
MSc student – TU Delft

Timo Warmenhoven
MSc student – TU Delft

Laurène Massa
MSc student – TU Delft

Alina Bekkering
MSc student – TU Delft

Christiaan Oosterom
MSc student – TU Delft
Graduated MSc students

Thieme Arkema (2025)
MSc student – TU Delft
Thesis: The Development and Validation of In Vivo Optimal Fiber Length Measurement

Iris Kan (2025)
MSc student – TU Delft

Yoni Gouka (2025)
MSc student – TU Delft
Thesis: Development of a Female-based Musculoskeletal Model of the Lower Extremity

Martin Miranda Marquez (2024)
MSc student – Leiden Universiteit
thesis: Automatic human body mesh registration
from three-dimensional scans

Ragnhild Maarleveld (2024)
MSc student – TU Delft
Literature Review: What the %PCSA? Addressing Diversity in Lower-Limb Musculoskeletal Models: Age- and Sex-related Differences in PCSA and Muscle Mass

