Contact information
Research groups
Jack Tu
BSc, MSc, PhD
Research Fellow in Clinical Biomechanics
- ORUK Early Career Research Fellow
Clinical Biomechanics researcher interested in body movements and therapeutic mechanisms.
I am a postdoctoral researcher at the Oxford Orthopaedic Engineering Centre. I work with Assoc Prof Stephen Mellon on a fusion development of ultrasound imaging and motion-capturing techniques for human movement analysis. This project is supported by Versus Arthritis, and we aim to develop a novel method to offer quick and robust joint position assessments in 3-D, which can help improve the efficiency of clinical examinations. I recently received an Early Career Research Fellowship from Orthopaedic Research UK, which will support our further clinical data collection and verification with the system.
While studying for my PhD at Queen Mary, University of London, I developed an ultrasound-based semi-automatic computer tracking method to quantify soft-tissue dynamics to validate in-vivo effects and taping therapy mechanisms. Before my PhD, I was trained in the Sports Medicine discipline at Kaohsiung Medical University, followed by clinical and research-based master studies in Kent and Taiwan. I am now particularly interested in discovering the effects and mechanisms of musculoskeletal disorders and therapies with computer-aid clinical data analysis.
In addition to my research works, I coordinate and lecture on Biomechanics in the MSc in Musculoskeletal Sciences. In my spare time, I am a fan of new technology and sports; I watch F1 and listen to both pop and baroque music. I am also a serious coffee lover who is passionate to develop home barista skills.
Recent publications
DARK: dynamic graphs based angle-aware registration of knee ultrasound point clouds
Chapter
Hwang I. et al, (2025), 87 - 97
DG-PPU: dynamical graphs based post-processing of point clouds extracted from knee ultrasounds
Conference paper
Hwang I. et al, (2025), 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs.
Journal article
Tu SJ. et al, (2025), Knee, 52, 212 - 219
3D freehand ultrasound using visual inertial and deep inertial odometry for measuring patellar tracking
Conference paper
Buchanan R. et al, (2024), 2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1 - 6
Optimizing the tibial keel slot for the Oxford unicompartmental knee arthroplasty
Conference paper
Arthur L. et al, (2024), Orthopaedic Proceedings, 106-B, 119 - 119