Research Overview

My research focuses on machine learning and deep learning for wearable sensing and human gait analysis, with particular interests in spatiotemporal modelling, multimodal time-series learning, and intelligent healthcare applications. I investigate how plantar pressure and gait-related sensor data collected from smart insoles can be analysed to understand human movement patterns and identify potential gait abnormalities. My work explores Transformer-based architectures, attention mechanisms, and sensor data fusion for gait recognition and movement assessment. The broader aim of my research is to develop robust AI methods for continuous gait monitoring and health assessment, with potential applications in Parkinson's disease and lower-limb disorders.