Telehealth system for fall prevention
Master's project - IN PROGRESS
Researching and developing a closed-loop telehealth system for fall prevention in the elderly, using the state-of-the-art Intel RealSense D435 depth sensor with skeletal tracking AI for fall risk prediction, and the Oculus Quest 2 VR headset for novel data visualisations.
This is being completed as my Master's solo project. To date, I have written a C# algorithm to calculate and analyse key biomechanical paramters of a sit-to-stand motion using skeletal data obtained in real-time from an AI engine running in conjunction with the stated depth sensing camera. The algorithm then uses this output to make fall risk predictions based on the analysed biomechanical performance of the user. I have since developed a practitioner web application (as seen below) using HTML/CSS/JS with a backend NodeJS server to visualise the fall risk associated with the sit-to-stand motions of the elderly user. This application provides novel insight to the practitioner about the fall risk of the remote patient, which phase of motion is causing the most risk, and which body parts of the patient should subsequently be trained to reduce their risk of falling at home.
I am now in my final stage of development, working on a VR application to create immersive data visualisations of the subsequent risk data. Please check back here for future project updates...