Integrating Wearables, Biomedical Imaging & Computational Biomechanics: Application to MSK Modelling

Abstract

In this presentation I will showcase the work from my team over the last 5 years at the Auckland Bioengineering Institute, University of Auckland, NZ.

Our team has developed wearable technologies integrated with machine learning methods to predict lower limb biomechanics outside the gait lab. Integrating this with imaging methods including MRI, CT, and computational methods including statistical shape modelling, and finite element informed bone remodelling, we have explored health and sports related applications.

I will showcase some interdisciplinary applications including musculoskeletal adaptation in barefoot running, infant foot development during the first year of walking, and the traditional practice of the bound foot. I will then explore some recent work in orthopaedics whereby we followed patients after joint replacement and the insight we have learned from integrating wearables and modelling into the rehabilitation phase.

Biography

Justin Fernandez is a Professor in the Department of Engineering Science and Biomedical Engineering, and the Auckland Bioengineering Institute, University of Auckland, NZ.

He graduated with a Bachelor of Engineering (Honours first class) in Engineering Science (1999) and a PhD in Bioengineering from the University of Auckland (2004). Between 2005 and 2010 he completed an ARC postdoctoral fellowship at Melbourne University in Mechanical Engineering and was a research scientist at the CSIRO (Monash University). From 2011 to 2013 he was an Aotearoa Fellow funded by the Robertson Foundation in the Auckland Bioengineering Institute.

He is a Principal Investigator in the musculoskeletal modelling group where he leads research into computational biomechanics, orthopaedics, sports science, forensics, brain mechanics, and ADHD. His work integrates biomedical imaging, gait analysis, wearable sensors, large population data, statistical models, and computational mechanics. His research strongly focuses on clinician engagement.

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