This project develops a dual-modal sensing system combining Near-Infrared Spectroscopy (NIRS) and Diffuse Correlation Spectroscopy (DCS) to enable comprehensive, real-time monitoring of cerebral blood flow and tissue oxygenation. By integrating these two optical techniques, the system aims to provide a more complete and accurate assessment of cerebral perfusion and oxygenation dynamics.

This project focuses on integrating artificial intelligence (AI) with wearable sensor data to enable real-time, non-invasive diagnosis of blood flow and oxygenation. By leveraging advanced machine learning algorithms, the system will analyse the optical sensing data to identify patterns and anomalies associated with various physiological conditions. AI will enhance the accuracy of the data interpretation, providing a more reliable and efficient tool for healthcare monitoring.

This project aims to develop a fibre-optic ductoscopy system for the early detection of ductal carcinoma in situ (DCIS) by integrating optical sensing and soft robotic technology. Combined with a soft robotic platform, the system offers minimally invasive, real-time imaging with high precision. The goal is to enhance breast cancer diagnostics, reduce the need for biopsies, and enable earlier intervention, ultimately improving patient outcomes.