Sports Technology, Digital Solutions and Data Analytics (LSPA_632)
About Study Course
Objective
Develop in-depth knowledge and practical skills in the use of sports technologies, digital solutions and data analytics in planning, managing and evaluating the training process. The course encourages data-based decision-making using smart devices and sensors that allow athletes to be evaluated outside laboratory conditions -- in training and competitive environments. AI and machine learning techniques in data processing, model building and athletic performance prediction are also being learned.
Prerequisites
• Basic knowledge of sports science and training process planning.• basic knowledge of statistics and data processing.• Prior experience with digital tools (e.g. Excel, SPSS, RSTUDIO Statistics) is desirable.
Learning outcomes
1.Acquire knowledge of the use of sports technologies and digital solutions in training, competition and rehabilitation processes, as well as the operating principles and capabilities of wearable smart devices in the acquisition of physiological data outside the laboratory. Students acquire knowledge of the basic principles of acquisition, structuring, processing and visualization of sports data, as well as the potential of VR/AR, video analysis and AI solutions and aspects of data ethics in the development of the training process.
1.1. Acquires and is able to process data from wearable devices and digital surveillance systems. Uses software tools for analyzing and visualizing sports data. Explain and discuss the results obtained in a reasoned manner.
2. Able to interpret biometrics and performance data by assessing workout load, recovery and athletic performance progress.
3. Uses artificial intelligence and machine learning tools, for simulating athletic performance and risk assessment.
4. Apply VR/AR scenarios and video analysis to improve the training process and evaluate athletes.
1.1. Independently analyse multimodal sports data and develop data-based recommendations for coaches and athletes to optimize the training process.
2. The reliability and practical applicability of sports technology data shall be critically evaluated based on scientific evidence-based literature.
3. Integrates AI-supported analytics into training planning and monitoring.
4. Able to collaborate on interdisciplinary teams with coaches, data analysts and technology experts.
5. Ensure the use of sports technologies in accordance with ethical and data protection principles (including GDPR).
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Sports Science | 2 | Master's | Required |
