Dr Khuong An Nguyen joined the University of Brighton as a Lecturer in Computer Science in 2020. He is the winner of several awards – ‘Most outstanding Vietnamese Student in the UK 2014’, ‘Most Inspirational Lecturer of the Year 2021’, ‘Outstanding Research Supervisor Award 2022’ and the University of Brighton’s ‘Rising Star Award 2022’. He has also attracted numerous research grants in order to further his work. His research into digital healthcare and pandemic prevention have generated interest from industry, government and academia.
Here, Khuong talks about his research and explains how his teaching and research build on each other.
Digital healthcare
‘In 2003, my grandma passed away with severe dementia. At the time, there was no existing technology to help communicate her daily needs, nor her whereabouts for the carer to attend to her swiftly. It was a struggle for my family to provide my grandma with the end-of-life care that she deserved.
‘Since my very first day as a PhD student, it has always been my dream to develop a healthcare monitoring package for dementia patients. In the course of the past seven years, every paper and journal I have written was a step closer in making this a reality.
‘My late grandma liked walking, but she found it difficult to walk without a supporting stick. In her final years, she suffered four major falls, and many minor bruises, while unable to alert the carer. A wearable motion sensor could detect such an event. In my previous works, I had implemented several Machine Learning models to classify human activities from sensor time-series measures and this type of digital technology could lead to the creation of support for detecting and caring for falls and other accidents of this type.
‘My grandma often wandered around in a disoriented manner. At times, it was challenging for my family to locate her quickly in our 3-storey house. In my previous works, I proposed to survey a training database of the signals at every location, generated by the Bluetooth beacons installed at fixed locations in the house. To locate the patient, the system matches the Bluetooth signals collected by the wearable sensor at its current location with the above training database.
‘National importance wise, healthcare is one of the top research priorities for the UK, which has committed £135 million in health research between 2020-2025 as a concrete action in supporting this need. Most importantly, the funding is dedicated to three major areas, including dementia, which this project aims to tackle. Therefore, the development of novel, well-calibrated Machine Learning based solutions for dementia care, is key to maintaining the UK’s world-leading position in this era of Machine Learning, and to success in growing the UK’s Artificial Intelligence and data-driven economy.
‘It is too late for me to help my grandma. However, seeing my work make a difference to many other children’s grandmas in the world would, hopefully, make my grandma smile, were she still here.
Future pandemic prevention
‘Building on from my research into digital healthcare for dementia, I have more recently been working on technology to address pandemic spread, such as we’ve seen with Covid-19.
‘The current COVID-19 global pandemic has caused widespread human suffering and strongly highlighted the need for better preparations for future outbreaks, including a more efficient test and trace system. Currently, most Bluetooth based contact tracing systems may only detect synchronous contacts; that is, it detect person-to-person virus transfer, when two or more people are in the same place at the same time. What it does not address is ‘asynchronous’ infectious contacts. ‘Asynchronous’ infectious contacts happen when someone touches a contaminated surface, the virus having been left on that surface by an earlier infected visitor to that location. These infectious contacts are challenging to identify, because precise location tracking is both unavailable indoors without GPS signals, plus location tracking is intrusive to the user’s privacy. Critically, many airborne viruses, such as COVID-19, may survive on certain materials such as cloth fabric for up to four days, and on stainless steel, glass and latex for up to 7 days.
‘In 2021, I proposed a novel idea for detecting asynchronous contacts, using smartphone sensor traces, including the accelerometer, magnetometer, barometer and acoustic sound. The inspiration was that when two phones visited the same place, their respective sensor measures of the environment could be similar. The proposed tracing model works as follows: In the first phase, a smartphone app will be developed to collect the sensor measures silently in the background. These data do not contain any intrusive location history, nor ever leave the phone without the user’s consent. In the second phase, when a positive test is confirmed, the user may release the sensor logs for other participants to locally match their private traces for asynchronous contact detections.
‘In this way, when the sensor information on one phone matches that on another, we can tell that those two people have been in the same place without tracking them geographically or knowing what or where the place visited is. It is about matching environment, rather than movement tracking.
PhD supervision
‘Alongside my research I am also dedicated to my role as supervisor; it is woven into my research work. When I accept a PhD student, it is not only about the specifics of their research, but also about how their research could make an impact on society, and most importantly, to their personal value and aspiration.
‘Finding a novel research topic isn’t hard, but finding the personal meaning of the research is the true purpose of the PhD journey.
‘My role as a supervisor is to help the students to discover their aspirations, to uncover their potentials, and to strengthen their talents for a future career in either academia or industry. Within five years of getting my own PhD in 2017, I have helped one student to achieve his PhD, and am supervising another four PhD students, three of them as lead supervisor.’
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