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My expertise covers the whole product development process, from designing hardware like microscale transdermal platforms and instrumented arthroplasty implants to signal processing and data analysis. I enjoy exploring biomarker trends in both population and individual data, such as obesity rates and real-time heart and respiratory predictions using AI modelling.
You can check out my publications here: Google Scholar and ORCID.
Microneedle drug delivery and biomarker sampling platform
Microneedle drug delivery and biomarker sampling platform
My PhD thesis explores how microscale medical devices, which deliver treatments or measure health indicators through the skin, can be better designed and tested to work effectively in humans. The aim is to create guidelines to improve device performance. These devices, often first researched on animals, don't always perform the same way in humans due to the complexity of human skin. I developed biomechanical and diffusion kinetic models that scale across species allometrically. Additionally, I used 3D multiphoton microscopy to capture vaccine diffusion and cellular uptake, dynamically tracking vaccine concentrations and skin recovery. This research was conducted in collaboration with another lab at the UQ Frazer Institute.
A highlight of my PhD was presenting my work at the European Society of Biomechanics in 2016 (Lyon), attending the TU Graz summer school on biomechanics and conducting a clinical study in Linköping, Sweden.
The goal is to make the development process faster, cheaper, and more accurate, reducing the need for animal testing and ensuring these devices can move from the lab to clinical settings more efficiently. Prof Mark Kendall (now ANU) and A/Prof Michael Crichton (now HWU) were my supervisors.
You can read my thesis here. The DOI is 10.14264/uql.2018.594.
Instrumented hip implant
Instrumented hip implant
During my postdoctoral research at Delft University of Technology, The Netherlands, I led a project developing instrumented hip implants for intraoperative tissue stability and balancing. These implants utilised optical, electromagnetic, and inertial-based sensors, powered by Arduino, to provide surgeons with real-time information for objective decision-making. I was involved in the design (using Solid Edge CAD models), manufacturing, and experimentation phases of the project. This work, supported by our industry stakeholder Johnson & Johnson, led to successful priority date patent filings and obtaining governmental funding to launch a startup.
We explored different versions of these sensors, ranging from strain gauge Stewart platforms to compressible polymer Hall sensor-based designs. Additionally, we investigated using acoustic resonance to predict the quality of implant insertion. Our preclinical trials were conducted in collaboration with SINTEF in Norway and CCMIJU in Cáceres, Spain. Prof Jenny Dankelman and A/Prof Tim Horeman were my supervisors.
Vital signs patch platform and data modelling
Vital signs patch platform and data modelling
Image above from 2M Engineering
Not many pictures to show as the work is mostly done on the computer! As a Health Data Scientist at TNO, I analysed personal and population-level health data, including cardiovascular biomarkers from medical-grade wearables and COVID-19 impact predictions. The health patch shown in the picture is a research-grade clinical wearable device used to monitor the user's heart rate, heart rate variability, and respiratory rate. This experiment was conducted on Dutch military pilots performing a set of exercises. The device was compared to two commercially available gold standard devices, and the data was statistically analysed for accuracy and consistency. Collaborating with the Dutch Ministries of Health and Defence, industry partners, and startups, I utilised R, Python, and Matlab for signal processing and modelling. This role enhanced my coding expertise in statistical and predictive methods. Additionally, I secured funding to develop a data platform for medical device clinical studies.
Apps that I use regularly (and less frequently) for research
Apps that I use regularly (and less frequently) for research
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