AI Functional Clothing Design for Patients with Spinal Deformities

This study is a multi-disciplinary collaborative project which involves experts in computer science, orthopaedics, materials science, garment technology, engineering and biomechanics. The team will endeavour to develop artificial intelligence-designed functional clothing for patients with spinal deformities. It will do so by machine learning (ML) methods that involve collecting and inputting patient datasets and parameters of functional clothing for machine training. Academics, clinicians and AIS patients will benefit from the project, and the project outputs can be extended to the application of ML in biomechanical research in which the generated automated solutions can increase the accuracy and repeatability of the execution of critical tasks.

Project Leaflet

Fok, Q., Yip, J., Yick, K. L., & Ng, S. P. (Accepted/In press). Design and fabrication of anisotropic textile brace for exerting corrective forces on spinal curvature. Journal of Industrial Textiles.
Liang, R., Yip, J., Fan, Y., Cheung, J. P. Y., & To, K.-T. M. (2022). Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches. International Journal of Environmental Research and Public Health, 19(3), 1177.
Fok, Q., & Yip, J. (2021, July). Applying Numerical Simulation to Predict Effect of Brace Wear for Scoliosis. In International Conference on Applied Human Factors and Ergonomics (pp. 217-223). Springer, Cham.
Liang, R., Yip, J., To, K. T. M., & Fan, Y. (2021, July). Machine Learning Approaches to Predict Scoliosis. In International Conference on Applied Human Factors and Ergonomics (pp. 116-121). Springer, Cham.

Principal Investigator
Dr Joanne YIP 

Collaborating Investigators
Dr Zerence NG
Dr Kit Lun YICK

Dr Brain CHEN 
Dr Jason CHEUNG 
Dr Mei-Chun CHEUNG 
Dr Fang He LI