Industrial Transformation Research Hubs - Grant ID: IH210100040
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC RESEARCH HUB FOR CONNECTED SENSORS FOR HEALTH. This Hub aims to develop, manufacture and deploy high-tech, cyber-secure, medically-certified IoT sensors to global health markets by integrating disparate Australian capabilities into a productive end-to-end value chain. This Hub expects to position Australia at the forefront of connected health by integrating sensor science with cyber-secure data analytics, regulatory approval and certified manufacturing capabilities. Expected outcomes of this ....ARC RESEARCH HUB FOR CONNECTED SENSORS FOR HEALTH. This Hub aims to develop, manufacture and deploy high-tech, cyber-secure, medically-certified IoT sensors to global health markets by integrating disparate Australian capabilities into a productive end-to-end value chain. This Hub expects to position Australia at the forefront of connected health by integrating sensor science with cyber-secure data analytics, regulatory approval and certified manufacturing capabilities. Expected outcomes of this Hub include advanced manufacturing capacity for connected sensors, strategic partnerships and commercialisation skills to translate sensors research to create economic benefits such as jobs and locally-made products for domestic and export markets, as well as improving the health of Australians.Read moreRead less
Signal processing algorithms for interpreting multi-dimensional ambulatory data during normal activities: correlates of current measures of fall risk. This project will develop algorithms to analyse human movement, measured using a small waist-worn sensor, which approximate existing clinical tests to identify likely fallers. This will enable future fall risk monitor development. This is an important problem as one in three senior citizens fall each year, costing around $500 million in healthcare ....Signal processing algorithms for interpreting multi-dimensional ambulatory data during normal activities: correlates of current measures of fall risk. This project will develop algorithms to analyse human movement, measured using a small waist-worn sensor, which approximate existing clinical tests to identify likely fallers. This will enable future fall risk monitor development. This is an important problem as one in three senior citizens fall each year, costing around $500 million in healthcare.Read moreRead less