People

Lloyd Chan

Lloyd Ly Chan is a PhD candidate at UNSW Sydney. Before pursuing his PhD studies, he obtained a BSc (Hons) degree in Physiotherapy, an MSc degree in Epidemiology and Biostatistics (Salutatorian), and gained experience as a physiotherapist in public hospitals. Specializing in neurological rehabilitation, predictive modelling and machine learning, Lloyd’s research focuses on wearable technology for assessing and intervening in mobility impairments in individuals with neurological diseases. He utilizes signal processing techniques, large-scale health database records, and machine learning algorithms to quantify mobility quality and investigate the association between mobility impairment and neurodegenerative diseases.

Lloyd’s notable contribution during his PhD project was the development of the Watch Walk algorithm, which accurately measures walking speed, quality, and distribution using wrist accelerometers. This algorithm, validated in a database of over 70,000 individuals, has demonstrated high reliability and precision. Leveraging the Watch Walk algorithm, he conducted research on the association between baseline mobility performances and subsequent depressive episodes, providing valuable insights applicable to clinical practice. Additionally, Lloyd’s work has led to the prediction of injurious falls, with implications for fall prevention strategies.
Acknowledged for his outstanding research, Lloyd has received one governmental scholarship and two philanthropic grants totaling $230,000. The impact of his work extends beyond academia, as the Watch Walk algorithm is now being utilized in Day Rehabilitation Centers and private physiotherapy clinics in Hong Kong. Lloyd’s Social Innovation project, with a grant of $90,000, aims to implement personalized, predictive healthcare in elderly homes. His research findings have been published in first-quartile peer-reviewed journals, including notable publications such as the Journal of the American Medical Director Association and the Journal of Neurological Physical Therapy.

In addition to his PhD project, Lloyd has actively collaborated with individuals diagnosed with Parkinson’s disease. He contributed to a randomized controlled trial evaluating the effectiveness of a peripheral-electrical-stimulation-based smart garment in overcoming freezing of gait. Lloyd played key roles as a blinded clinical assessor, physiotherapist, and bio-statistician, contributing to study design, evaluating clinical progress, and generating statistical reports. He has also been involved in a study utilizing waist-worn motion sensors to identify Freezing of Gait, contributing to the recruitment of the largest sample size to date and the development of a real-time API for detecting FOG, which has been integrated into commercial health products.

Looking to the future, Lloyd’s research interests lie in developing a wrist-worn sensor to accurately evaluate walking speed, quality, distribution, and distance covered in individuals recovering from stroke. By addressing the current limitations in subjective walking evaluation methods, this technology aims to provide objective and comprehensive assessments to inform intervention strategies and accurately evaluate recovery progress in stroke patients.


More people