Project Mosaic

Project Mosaic aims to develop and implement an automated, AI- driven solution to improve the efficiency and reliability of the clinical gait analysis process.

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Team members:

  • Patrick Gu, BEng(ECSE)(Hons) & BBiomedSc

  • Avinash Tatsat, BEng(Chem)(Hons) & BPharmSc

Clinician:

  • Nikolaos Darras, Monash Health Kingston Centre

  • Corey Joseph, Monash Health Kingston Centre

Funding:

Healthcare Innovation Summer Scholarships (HISS) Phase 2 funding, Monash Institute of Medical Engineering

Project History

The project was brought to the Healthcare Innovation Summer Scholarship 2021-22 (HISS) program by our clinical champion Nikolaos Darras.

 

Under the HISS program, we scoped and validated our problem via background research and numerous national and international stakeholder interviews, which yielded information that has and will continue to help us in building our solution. Our business-case was fleshed-out with advice from our academic advisors, and our technical solution, including our data pre-processing and deep-learning pipeline underwent significant development.

 

Our project’s graduation from the HISS program and continuation under the MYMI Projects Branch marks a significant milestone in our project’s journey to create a real-world deep learning solution for the automatic detection of gait cycle events in patients with pathological gait.

 

Currently, our project’s focus is on the technical development of our deep-learning solution, including hyperparameter optimisation and other necessary steps.

Goals

Our main priority for this year is to complete the technical development of our solution, after which we can begin real-world user-testing at our clinical champion’s gait analysis clinic. Paper publication would be a subsequent goal after experimental results are obtained.

Plans

We’re fortunate in that we have access to a large support network consisting of academic advisors, fellow MYMI Projects members, and other individuals in the wider Monash community that we can leverage in order to support the success of our project. With this in mind, we plan to continue the consistent development of our data pre-processing and deep learning pipeline, being sure to access any required resources and support on the way.