Project description
Sweating to the beat of semantic analysis
More and more Europeans are inserting health-enhancing physical activity into their daily routine. To achieve better results, personal trainers strive to improve their competences and techniques, especially related to endurance training, rehabilitation and health precaution. Even though it is not easy, the existing amount of data from different fields could revolutionise training methods and bring new useful tools. The EU-funded MyGoalMEDAL project aims to engage machine learning in an interdisciplinary semantic analysis of existing international data from connected fields. The goal is to systemise metadata from all promising experiences, leading to disruptive innovations with new tools and applications for e-health and sports workouts.
Fields of science
Programme(s)
Funding Scheme
SME-1 - SME instrument phase 1
Coordinator
PRIEBE MATHIAS
Net EU contribution
€ 50 000,00
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Other funding
€ 21 429,00