ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

appropriates mechanical properties and boundary conditions are assigned to mesh
elements. The results of biomechanics studies [15,16] are used to define the mechanical
parameters, while boundary conditions are defined by selecting the mesh elements
consistent with external and geometric constraints.
Fig. 2: Proposed approach based on computer graphics techniques.
An optical motion capture system (Vicon MX 13i, Oxford Metrics, UK) is used to
obtain bone poses of the subject 3D models. Compared to others techniques (e.g. intracortical
pins [17], external fixators [18]) this modality is non invasive and allow to
record large ranges of movements. However, the internal bone remains inaccessible and
the estimated poses integrate errors due to the relative displacements of individual
markers which known as soft tissue artifacts (STA) [19]. To optimize the effects of
STA, we apply a mathematical method based on non-linear optimization and joint motion
constraints [20].
We exploit neuromuscular simulations for estimating the subject joint reaction force
[21]. Our neuromuscular simulation pipeline starts by scaling a generic model to match
the anthropometry of the subject. In this step, data resulting from different
measurements (e.g., markers positions, MRI, subject weight, etc) are used to achieve
accurate scaling. Then, the scaled model and motion capture data are used in an inverse
kinematics (IK) step to calculate joints parameters related to the markers positions. The
scaled model and IK output are used in the inverse dynamics (ID) approach to estimate
the muscles activations which produce the movement. Finally, results of previous steps
are used in analysis approach to estimate the loads on the subject hip joint.
To calculate the anatomical models deformations, we use a specific simulation model
[13,14] based on finite element method (FEM). Different criteria are considered in this