David Chan

dcha704-david-chan-web-pb

ME student

Contact details

Uniservices House
70 Symonds St
Auckland

Phone:

Biography

My current highest qualification is a BSc (Hons) with First Class Honours majoring in Statistics. I've received summer research scholarships to work with COMPASS (Faculty of Arts) in 2015-2016 and Professor James Curran (Department of Statistics) in 2016-2017.

Thesis

Zoning in on Pressure

One in four women has some form of a pelvic floor disorder such as pelvic organ prolapse or urinary incontinence. Current research in the field shows that pelvic floor muscle training is a preventative measure against pelvic floor disorders, in particular urinary incontinence. Although devices exist to help with pelvic floor muscle training, they are bulky and provide very limited quantitative data. At the Auckland Bioengineering Institute, a new intra-vaginal pressure sensing device has been created. Using eight pressure sensors, the device records a pressure profile along the length of the vagina. This project is focused on how to analyse the pressure waveform data captured by this device. To date, there is no precedent of how to examine the pressure profiles statistically, or how to integrate these statistics into feedback for the user of the device about the status of their pelvic floor.The starting point is a pre-processing algorithm for handling the data, which, with the help of human intervention, segments the recorded data into time intervals as a user is guided through a series of prescribed activities. This issue is problematic because of the large volume of data generated by the device and many possible deviation from the real series of activities performed compared to the prescribed sequence expected. With two clinical trials about to start, data will be arriving in large volumes and processing needs to be automated. Once segmented, this project will address how to analyse the pressure waveforms to extract parameters that relate to pelvic floor health from data which includes variables such as device placement, inconsistency of performing the same activity. Methods of presenting feedback on pelvic floor parameters will be considered with possible implementation in the FemFit Android app that accompanies the devices. In summary, this thesis seeks to improve and automate the pre-processing algorithm and ultimately to propose a statistical model to evaluate the change in the pressure profiles generated by the device, following an intervention.

Supervisors

Research Group