[Cite as http://purl.org/au-research/grants/arc/DP150100116]
Researchers Dr Markus Hagenbuchner; Dr Stewart Trost; Dr Dylan Cliff
Brief description This interdisciplinary project explores novel machine learning approaches to modelling physical activity monitor data in preschool children. The approach taken is considered the future of physical activity assessment and is expected to substantially enhance the measurement of physical activity and the evidence base that informs strategies to improve population health through physical activity promotion. The project will transform our understanding of young children’s physical activity behaviour, and will have important implications for the design of accurate and effective technology-based physical activity monitoring and intervention applications that could be delivered through the e-health initiative in Australia.
Funding Amount 286424
Funding Scheme Discovery Projects