Open Source Load Monitor
The aim of the project is the results of the Raak project Big data technology for detecting athletes overload,
available to a wider audience. During the project, a prototype was developed of a monitor that maps the load on athletes. This tool helps trainers and athletes to quickly gain insight into possible over- and underloading of the athletes in order to create an optimal training program that minimizes the risk of injuries and maximizes fitness.
Machine learning is used to create prediction models based on the data supplied by the respective consortium partners. Because the sports clubs use different sensors for the movement data and use different questionnaires for wellbeing and training intensity, the models must be made suitable for each club separately.
Within this project we are creating an open source environment where interested parties can use the general part (the user interface and the methodology for machine learning), where they can make an adjustment for the data part for their own organization. In this way we want to further disseminate the knowledge gained in the project and offer the opportunity to develop it further. See the Load Monitoring and Prediction tool for Team Sports Coaches: https://github.com/SaxionAMI/AthleteLoadMonitor.
The project is carried out by Saxion University of Applied Sciences, Ambient Intelligence research group.
September 2020 until August 2021
Dr. Tatiana Goering-Zaburnenko, 06 2011 3941, firstname.lastname@example.org
This project is financed by SIA