Vacature

Are you passionate about shaping the future of healthcare? Do you want to develop technology that truly matters in people’s lives? Join TU Delft in creating radical innovations that support patients at home

Job description

The BODIES group, at the department of Biomechanical Engineering TU Delft seeks a motivated postdoctoral researcher for a full-time, fully funded, 36-month project on privacy-preserving, single-camera markerless motion capture for clinical assessment and long-term home monitoring of motor impairments in stroke and Parkinson’s disease patients. The position is based at TU Delft, with part of your time spent on-site at Model Health (Leuven, Belgium) for the development of the video-based prototype. You will work closely with Model Health and Moveshelf, embedded in the multidisciplinary “Care is coming home!” program, a 5-year consortium of 2 technical universities (TU Delft, UTwente), 6 hospitals, 13 companies (including Model Health and Moveshelf), 1 rehabilitation centre, and 5 patient organisations.

The Challenge

Reliable, long-term monitoring of motor impairments outside the clinic is essential to support home-based rehabilitation, yet existing markerless motion capture systems are rarely validated on neurological gait patterns and struggle to generalize across pathology, body morphology, camera viewpoint, and home environments. This position aims to close that gap: you will develop and validate a novel single-camera markerless motion capture system, combining biomechanical realism, synthetic data generation, and privacy-preserving visualization within a single framework, to enable both clinical assessment and secure, long-term home monitoring.

You will:

• Validate the current markerless motion capture system against marker-based data in stroke patients

• Generate biomechanically accurate synthetic video data to expand training data and enable controlled sensitivity analyses across pathology severity, body morphology, camera viewpoint, and environmental conditions

• Develop a single-camera, home-based markerless system

• Test the algorithm across lab and home-recorded environments to assess real-world performance

• Design clinician-facing visualization tools ensuring privacy-by-design anonymization of patients and their home environment

You will translate this into a research plan aimed at peer-reviewed publications, alongside consortium deliverables