
The Use of Applied Technology in Team Sport
Description
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The Use of Applied Technology in Team Sport describes and assists researchers, academics and professionals with understanding the methodology around applied technology in sport, examining what systems track players' performance and who are the manufacturers that provide these systems.
This new volume goes on to describe how to apply the systems, highlights the ways of reporting analysis information and helps the reader to know and understand the future avenues of research and development.
The Use of Applied Technology in Team Sport is considered an essential guide for researchers, academics and students as well as professionals working in the areas of Applied Sport Science, Coaching, and subjects relating to Physiology, Biomechanics, Sports Engineering, Sports Technology and Performance Analysis in Sport.
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Persons
Markel Rico-Gonzalez is a researcher of both University of Basque Country, Spain, and BIOVETMED & SPORTSCI Research Group from University of Murcia, Spain. He has a Ph.D. in Sport Sciences- Team Sports Performance and Applied Technology and Applied Technology in Faculty of Education and Sport from the University of Basque Country.
Content
1. Standarization Of Electronic Performance and Tracking Systems
2. Semi-Automatic Multiple Camera Systems
3. Global Navigation Satellite Systems
4. Local Positioning Systems
5. Microelectromechanical Systems
6. Wearables for the Internal Workload Monitoring: Combined Hear Rate and Mechanical Sensors
Part II: Data Transmission
7. Data Acquisition and Transmission
8. Real-Time Feedback: An Update
Part III: Data Processing: Performance Variables
9. Kinematical Variables
10. Collective Tactical Variables
11. Neuromuscular Variables
12. Physiological Variables - Training Impulse and Heart Rate Variability
13. Multidimensional Variables
Part IV: Big Data and Reduction Techniques
14. Big Data Applied to Team Sport Athletes? Performance
15. Data Mining in Sports: The Case of Principal Component Analysis
Part V: Computer Vision and Machine Learning
16. Computer Vision
17. Machine Learning
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