Machine learning algorithms for physical activity from accelerometry and its quantification as predictor of progressive neurodegeneration
Approved Research ID: 62914
Approval date: July 27th 2020
We know that body and mind are highly intertwined and that daily exercise keep us fit and alert. It is also known that a healthy lifestyle where exercise is done regularly allow us to have better quality of life as we grow older. The aim of this project is to create new software algorithms that are capable of measuring our level of exercise and extract this information from wearable devices such as smart watches. The accurate measurement of our daily level of exercise, also known as physical activity, will help us to better understand the associations between exercise and a healthy brain and mind.
The proposed project will span a duration of 24 months, and during this time we will develop software for data processing, analysis and extraction of physical activity measures. We will also investigate the relations between the physical activity, brain volume and thinking ability.