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
1.1. Knowledge of sports technologies and their applicability in training, competition and rehabilitation processes has been acquired.
2. Knowledge of the operating principles of wearable smart devices (measures heart rate, heart rate variability, ventilation thresholds VT1, VT2, etc.) and the possibilities for data collection outside the laboratory has been acquired.
3. Is familiar with data acquisition, structuring, pre-production and visualization techniques in sports analytics.
4. Understand the potential of VR (virtual reality with and without VR headset)/AR (augmented reality) technologies, digital video analysis, and AI solutions to improve the training process.
5. Recognises the ethical and data protection aspects of the use of sports data.
1.1. Collect and process data from wearable devices and digital surveillance systems.
2. Use software tools (Excel, video Analyzer pro, Opencap, etc.) to analyze and visualize sports data.
3. Interpret biometrics and performance data by evaluating workout load, recovery and athletic performance progress.
4. Use AI and machine learning tools to model athletic performance and assess risk.
6. Apply VR/AR scenarios and video analysis to improve the training process and evaluate athletes.
1.1. To analyse independently multimodal sports data and develop data-based recommendations for coaches and athletes to optimize the training process.
2. Ability to critically evaluate the reliability and practical applicability of sports technology data.
3. Ability to integrate 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).
