Collection and AI-Based Analysis of Athlete Training and Wellness Data
Interested in sports? Technology used by real athletes? We have for a long time researched sports technology for performing performance monitoring for better results and injury avoidance. We developed running systems and tested them on real users. Want to be part of our research group and improve current systems? We are currently collecting a lot of data with respect to training load, wellness and injuries from elite clubs and the entire Norwegian superliga. This system uses a mobile app and servers in the cloud for storage and processing. This gives large opportunities to make systems to automatically analyze these data to see if we can improve how athletes train, avoid overuse injuries or how to select the best athletes for a competition.
We have for example started to look at machine learning approaches to give predictions, presenting the data and processing results to the trainers/coaches, how to add data from external equipment and physical tests, and system related aspects to improve performance and secure the users’ privacy. However, there are a lot of open questions and issues to address. Do you want to be a part of this? Contact us, and let’s see if we can find a thesis topic that matches your interests!
We collaborate with the leagues and elite clubs in Norway, Denmark and Portugal, the future female football center in Tromsø, the football associations, Forzasys (system provider) and several international partners.
Project tasks can include but are not limited to the following:
- Mobile application development/testing: PmSys provides an Android/iOS mobile app which is used by the athletes to enter their health and training information daily. Interested students can contribute to the development and testing of these apps. We would like to increase their user-friendliness and usability.
- Web application development/testing: PmSys provides a web application called the “trainer portal” which aggregates athlete information for the trainers. Interested students can contribute to the development and testing of this application.
- Database setup and management: We would like to set up a SQL database to facilitate the automated analysis of the PmSys data. This task includes creating an accurate and efficient DB schema, populating the tables with existing raw data, establishing user and connection settings (SSH tunneling, etc.), and implementing a user-friendly solution for executing high-level queries ("view"s). Integration with a Python notebook/dashboard would be very useful.
- Data visualization and analysis:
- Explore different ways of visualizing athlete information on the trainer portal (test various metrics, aggregation methods, etc.).
- Visualize the occurrence of injuries in correlation with training sessions and wellness parameters.
- Conduct injury prediction using AI.
- Investigate the correlation between different health and performance metrics.
- Merge positioning/tracking data (from GPS measurements) with the PmSys dataset.
- Investigate the influencing factors on “wellness” (including sleep duration, sleep quality, stress, mood, fatigue, muscle soreness, etc.)
- Interdisciplinary research
- Time-series data analysis
- Machine learning / AI
- Data visualization
- Building systems
- Hard working, motivated
- Interested in learning (the rest can be learned during the thesis work)
- Pål Halvorsen
- Michael Riegler
- Cise Midoglu
- ForzaSys AS