Unlocking toddler activity insights: Introducing an easy-to-use machine learning tool for everyone
This presentation will showcase the latest version of my new open-source tool to assess toddler physical activity. This python tool allows non-experts to use a machine learning model without needing to code. It produces a physical activity summary from a raw accelerometer data file. Physical activity is essential for children’s healthy growth and development. The Canadian 24-hour Movement Behaviour Guidelines suggests that toddlers get 180 minutes of daily physical activity. To understand if toddlers are meeting guidelines, we must first accurately measure their activity. In my thesis work, I have developed a machine learning model that measures toddler activity. I have expanded this into a tool that can be easily used by those who work with toddlers, for example clinicians, researchers, and public health agencies.
Presenter Bio
Elyse Letts (she/her) is a PhD student in Medical Sciences at McMaster University with the Child Health & Exercise Medicine Program. Her research focuses on improving physical activity and sedentary time measurement in toddlers as well as investigating the impact of physical activity on toddler health outcomes. Prior to joining McMaster, she completed an undergraduate degree (BSc) in Kinesiology at the University of Waterloo.