Research Projects
Artificial intelligent 3D human pose estimation and generation with oclusion


Programme(s) to which this project applies:
☑ MPhil/PhD ☑ MRes[Med] ☒ URIS

Aim and Significance

The project aims to develop an occlusion-aware 3D human pose estimation and/or generation algorithm based on artificial intelligence techniques. Accurate human pose estimation is crucial for pose recognition, remote rehabilitation, disease management, etc. Such automatic pose estimation and generation techniques can have significant clinical impacts, providing accurate and fast clinical assessments, improving safety and efficacy, and providing continuous tracking.

Research Plan and Methodology

Research methods involved in this project would rank from model development, data management, software development, real-time sensing and analysis, clinical testing and validation. The developed software and algorithm can be transferable to other clinical expertise in the future.

 

Professor T Zhang, Department of Orthopaedics and Traumatology

Professor Teng Grace Zhang is a biomedical engineer with a medical background. Most of Grace’s research combines both disciplines by focusing on the modelling of biological systems with direct clinical applications including telemedicine, auto-diagnosis, surgical planning and tracking to facilitate real-time feedback with minimal radiations.

Currently,  Grace is an Assistant Professor, at the Digital Health Laboratory of the Orthopaedics and Traumatology Department, Clinical Medicine School, Faculty of Medicine, The University of Hong Kong (HKU). Previously, Grace has worked for nearly seven years as a Scientific Officer at the St George Clinical School of the University of New South Wales (UNSW, Sydney, Australia). All Grace’s tertiary education including her PhD (Australian Postgraduate Award) was completed at UNSW. Grace also has experience in running multiple instruments and drug trials funded by Medtronic and Sigma. She’s experienced in managing a new system developed as she worked in Kunovus Australia for two years as a system engineer prior to coming to the University of Hong Kong. Members of IEEE, EMBS, ABEC etc. The current ongoing projects include HMRF08192266 on light-based AI-driven malalignment quantification, HMRF19200911 on AIS digital health, PRP/078/21FX for surgical planning, AOSpine for automated spine malalignment screening, MRP/038/20X for non-radiation AI diagnosis and ITS/329/19 for anti-migration bone screws.  

Biography
HKU Scholars Hub
Lab
ORCID
tgzhang@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

  1. your CV,
  2. a brief description of your research interest and experience, and
  3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information. 

Research @ Department of Orthopaedics and Traumatology