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In partnership with Concord hospital and ResMed company, this project used sleep data analytics to diagnose and monitor patients with respiratory sleep disorder problems. The project resulted in two computational intelligence systems: one for sleep apnea diagnosis, and the other for patient monitoring using non-contact sensors. [real3dflipbook id=”2″]
This project is focused on type 2 diabetic patients, utilizing machine learning techniques to predict the onset of depression. Our goal is to provide early intervention and support to patients. [real3dflipbook id=”3″]
We’ve undertaken four separate projects in image processing, ranging from a new clubfoot classification system to cancer detection. This research has been carried out in collaboration with hospitals and research centres globally. [real3dflipbook id=”4″]
This project developed augmented reality exercises to aid physical rehabilitation. With child and senior-friendly versions, these exercises focus on muscle strength. They operate effectively with or without CI-based biofeedback, providing an interactive, user-friendly platform for effective recovery. [real3dflipbook id=’1′]
In partnership with the Brain Science Institute at RIKEN research institute in Japan and United Arab Emirates University, we’re utilizing computational intelligence to predict the effectiveness of physiotherapy exercises for individual rehabilitation patients. This project aims to fast-track recovery by personalizing exercise programs. [real3dflipbook id=”5″]
This project, in collaboration with Port Kembla Rehabilitation Hospital in NSW, focuses on the development of new rehabilitation devices for stroke recovery. We’re exploring bio-driven control and computational intelligence-based biofeedback with devices designed for hand and lower limb rehabilitation. [real3dflipbook id=”6″]
