François Chung, Ph.D.

Tag: motion capture

3DPH 2009 – Book chapter

3DPH 2009 – Book chapter

Publication

Jérôme Schmid, Anders Sandholm, François Chung, Daniel Thalmann, Hervé Delingette, Nadia Magnenat-Thalmann; Musculoskeletal simulation model generation from MRI datasets and motion capture data; In: Nadia Magnenat-Thalmann, Jian Zhang, David Feng (Ed.): Recent Advances in the 3D Physiological Human (3DPH), pp. 3-19, Springer-Verlag, London, 2009.

Abstract

Today, computer models and computer simulations of the musculoskeletal system are widely used to study the mechanisms behind human gait and its disorders. The common way of creating musculoskeletal models is to use a generic musculoskeletal model based on data derived from anatomical and biomechanical studies of cadaverous specimens. To adapt this generic model to a specific subject, the usual approach is to scale it. This scaling has been reported to introduce several errors because it does not always account for subject-specific anatomical differences. As a result, a novel semi-automatic workflow is proposed that creates subject-specific musculoskeletal models from Magnetic Resonance Imaging (MRI) datasets and motion capture data. Based on subject-specific medical data and a model-based automatic segmentation approach, an accurate modeling of the anatomy can be produced while avoiding the scaling operation. This anatomical model coupled with motion capture data, joint kinematics information and muscle-tendons actuators is finally used to create a subject-specific musculoskeletal model.

References

Publication

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Musculoskeletal simulation (INRIA project)
3D Anatomical Human (INRIA project)

Musculoskeletal simulation

Musculoskeletal simulation

INRIA project @Aalborg, Denmark (2009). Computer models and simulations of the musculoskeletal system are widely used to study the mechanisms behind human gait and its disorders. To create musculoskeletal models, the common method consists in using a generic model based on data derived from anatomical and biomechanical studies of cadaveric specimens. To adapt this generic model to a specific subject, the usual method consists in scaling it.

However, this scaling introduces errors because it does not account for anatomical differences between subjects. To resolve this issue, we propose a novel semi-automatic method to create subject-specific musculoskeletal models from Magnetic Resonance (MR) images and motion capture data.

First, an accurate modeling of the anatomy is produced using subject-specific medical data and a model-based automatic segmentation. Then, the anatomical model is coupled with motion capture data, joint kinematic information and muscle-tendon actuators to create a subject-specific musculoskeletal model.

This work is a collaboration between:

  • EPFL - École Polytechnique Fédérale de Lausanne (CH);
  • INRIA Sophia-Antipolis - Institut National de Recherche en Informatique et en Automatique (FR);
  • UNIGE - Université de Genève (CH).

The created musculoskeletal models can be used with any motion capture data to produce a specific simulation. The versatility of the framework is one of its qualities as it is not necessary anymore to rely on a standardized musculoskeletal model based on a healthy subject. It should be hence possible to simulate patients with disabilities as long as the acquired data reflect their pathology.

References

Related articles

3DPH 2009 (book chapter)
3D Anatomical Human (INRIA project)

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INRIA Sophia-Antipolis – Institut National de Recherche en Informatique et en Automatique