Many injuries occur in the Netherlands as a result of overload in all layers of the sport. How can these injuries be prevented? The approach of this project is the use of (sensor) technology and big data analysis for the early detection of signals of overload and thus the prevention of injuries.
A large amount of technology is already being used to measure athletes (quantified self). Professional sports clubs invest in expensive systems. In-depth interviews, however, show that there are two major problems: firstly, the large amount of data and secondly, the knowledge for a correct interpretation of the data required for a conversion into training advice.
Systematic data analysis
Computer models constructed from systematic data analysis of the enormous amounts of training data and supplemented with domain knowledge can solve these problems. There is a need for a system in which information from different sources is stored in one system and made accessible for subsequent integrated analysis. Individual profiles must be built from the data for fast, automatic interpretation. This allows border surveillance for overload to take place and training adjustments can be made where necessary.
Based on this need, the project focuses on the practical question “How can we develop a practically applicable tool that can validly measure the external and internal training load, helps the (para) medical staff and / or physical trainer to detect (potential) overload and thus helps to carry out the correct interventions for the prevention of injuries? ”.
The principle of such a "tax monitor" has already been demonstrated. For a fully-fledged prototype, however, both the computer model and the user application will have to be further developed, optimized, expanded and above all tested from a technical point of view. That is what the research questions of this project focus on. The focus is initially on (paid) football, but can also be translated to other team sports and recreational sports.
The Big data technology project for detecting athlete overload is funded by RAAK Publiek.
- Saxion (Lectoraat Ambient Intelligence & Lectoraat Gezondheid en Bewegen)
- Hanze (Lectoraat Praktijk-gerichte Sportwetenschap)
- UMCG (Centrum voor Bewegingswetenschap)
- Roessingh R&D
- FC Twente
- FC Groningen
- Heracles Almelo
- Stichting Topvolleybal Twente (Eurosped TVT)
- FysioCentrum Nijverdal
- Topvorm Twente
- Nederlandse Vereniging voor Fysiotherapie in de Sportgezondheidszorg (NVFS)
- 360SportsIntelligence en CE-Mate
February 2018 until July 2020.