Individual PhD Project Information:
Host institution: KU Leuven, Belgium
Supervisory team: Prof. Ilse Jonkers (PhD main supervisor, KU Leuven), Prof. Mathias Peirlinck (PhD co-supervisor, TU Delft), Dr. Sam Van Rossom (secondment host, Materialise Motion)
Enrolment in Doctoral School: Doctoral School of Biomedical Sciences, KU Leuven and Graduate School of Faculty the TU Delft Faculty 3mE.
PhD project description:
This PhD project will focus on identifying movement-related signatures of joint loading predisposing to degenerative joint disease based on a large prospective cohort study. The objectives are:
- to perform data collection to obtain knee and hip joint kinematics for a large elderly cohort (using Opencap, an open-source software combining computer vision, deep learning, and musculoskeletal simulation to quantify human movement dynamics from smartphone videos);
- derive movement primitives and associated ground reaction forces, using probabilistic principal component analysis;
- derive statistical shape modelling-based MSK models for the large elderly cohort;
- estimate contact pressures at the knee and hip joints for the elderly cohort;
- quantify inherent uncertainty associated with input parameters for the contact pressure estimates, using a surrogate model approach;
- construct SSM-based finite element models using contact pressures as inputs;
- calculate cartilage degenerative markers;
- determine disease-sensitive markers, using a machine learning approach such as smart regression algorithm.
A successful project will enhance our understanding of disease-sensitive biomarkers for cartilage degeneration in the knee and hip joints, develop a novel hybrid modeling workflow that incorporates parameter uncertainty, and establish a systematic, multidisciplinary computational approach to estimate joint contact pressures in these areas using smartphone video imaging.
Planned secondments:
- TU Delft: Focused on acquiring expertise in methodologies for research activities 3-5.
- Materialise Motion: Aim to provide the DC with industry-focused experience in developing innovative dynamic gait measurement systems for clinical assessments.