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Artificial Intelligence in Sport Performance Analysis [Pehme köide]

(University of Lisbon, Portugal), , (Université de Rouen, France), (Technical University of Lisbon, Portugal), (Research Unit for Sport and Physical Activity (CIDAF), Faculty of Sport Sciences and Physical Education, University of Coimbr)
  • Formaat: Paperback / softback, 196 pages, kõrgus x laius: 229x152 mm, kaal: 308 g, 4 Tables, black and white; 23 Line drawings, black and white; 1 Halftones, black and white; 24 Illustrations, black and white
  • Ilmumisaeg: 21-Apr-2021
  • Kirjastus: Routledge
  • ISBN-10: 0367254379
  • ISBN-13: 9780367254377
Teised raamatud teemal:
Artificial Intelligence in Sport Performance AnalysisSuurem pilt
  • Formaat: Paperback / softback, 196 pages, kõrgus x laius: 229x152 mm, kaal: 308 g, 4 Tables, black and white; 23 Line drawings, black and white; 1 Halftones, black and white; 24 Illustrations, black and white
  • Ilmumisaeg: 21-Apr-2021
  • Kirjastus: Routledge
  • ISBN-10: 0367254379
  • ISBN-13: 9780367254377
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367254377.html
Märksõnad:

To understand the dynamic patterns of behaviours and interactions between athletes that characterize successful performance in different sports is an important challenge for all sport practitioners. This book guides the reader in understanding how an ecological dynamics framework for use of artificial intelligence (AI) can be implemented to interpret sport performance and the design of practice contexts.

By examining how AI methodologies are utilized in team games, such as football, as well as in individual sports, such as golf and climbing, this book provides a better understanding of the kinematic and physiological indicators that might better capture athletic performance by looking at the current state-of-the-art AI approaches.

Artificial Intelligence in Sport Performance Analysis

provides an all-encompassing perspective in an innovative approach that signals practical applications for both academics and practitioners in the fields of coaching, sports analysis, and sport science, as well as related subjects such as engineering, computer and data science, and statistics.

Arvustused

"A unique synthesis of the very latest theoretical and methodological advances to identify and predict the complex patterns and relationships associated with successful sport performance. An essential read for all involved with enhancing the performance of individuals and teams."

Mike Court, Head of Recruitment Analysis Manchester United FC, UK

List of Figures
xi
List of Tables
xiii
Authors xv
Preface xvii
Acknowledgements xxiii
1 Empowering Human Intelligence: The Ecological Dynamics Approach to Big Data and Artificial Intelligence in Sport Performance Preparation
1(20)
Big Data in Sport
1(1)
Sources of Big Data
2(2)
Validity and Reliability of Big Data Measurements
4(1)
Grasping Big Data with Visual Analytics
5(1)
Design of Visual Analytics Systems
6(1)
Processing Big Data by Means of Artificial Intelligence
7(1)
Machine Learning
7(1)
Supervised Machine Learning
8(1)
Unsupervised Machine Learning
9(1)
Deep Learning
9(1)
Machine Learning and Behaviour Recognition
10(1)
Big Data and Sport Sciences: How to Converge?
11(1)
Abductive Method in Sport Sciences Research
12(2)
From Artificial Intelligence to Empowered Human Intelligence
14(1)
Conceptual Problems with the Term Artificial Intelligence'
15(1)
Human Intelligence
15(1)
Intelligent Sport Performance
16(2)
Intelligent Sport Performance Is Embodied
18(1)
Ecological Dynamics Approach Informs the Use of Artificial Intelligence in Sport
19(2)
2 How Is Artificial Intelligence Being Used in the Sport Sciences to Analyse and Support Performance of Athletes and Teams?
21(37)
Introduction
21(1)
AI in Sport Science: Research overview
22(2)
Predicting Performance
24(29)
Injury Prevention
53(1)
Pattern Recognition
54(1)
A Highlighted Source of Big Data for Artificial Intelligence: The Growing Impact of Automated Tracking Systems
55(1)
Conclusion
56(2)
3 From Reliable Sources of Big Data to Capturing Sport Performance by Ecophysical Variables
58(24)
Representative Assessment Design and Technology
58(3)
Design Methodologies for Data Collection
61(1)
Notational Analysis from Video-Based Systems
61(1)
Notational Analysis Principles
61(1)
Validity and Reliability of Notational Analysis
61(1)
Examples of Studies Looking at Validity and/or Reliability of Notational Analysis
62(2)
2D and 3D Automatic Tracking from Video and Optokinetic Multi-Camera Systems
64(1)
TV Broadcast Tracking Technology
65(1)
Multi-Video Camera Systems
66(2)
Optokinetic Camera Systems
68(1)
Sensors
69(1)
Smartwatch, Smartphone, and Global Positioning System (GPS)
70(2)
Inertial Measurement Unit (IMU)
72(2)
Validity, Reliability, and Accuracy of IMU
74(2)
Ecophysical Variables to Capture the Ecological Dynamics of Sport Performance
76(1)
Football
77(1)
Rugby
78(1)
Swimming
78(1)
Climbing
79(1)
Conclusion
80(2)
4 Computational Metrics to Inspect the Athletic Performance
82(31)
Introduction
82(1)
Individual Metrics
83(1)
Kinematic Measures
84(1)
Velocity
85(1)
Distance
85(1)
Orientation
85(1)
Trajectory Entropy
86(1)
Fractional Dynamics
87(2)
Physiologic Metrics
89(1)
Heart Rate
90(1)
Electromyography
91(1)
Muscle Load
92(1)
Electromyography Root Mean Square
93(1)
Electromyography Fourier Transform
93(1)
Group Metrics
94(1)
Spatial-Temporal Metrics
95(1)
Weighted Centroid
95(1)
Weighted Stretch Index
96(1)
Effective Surface Area
97(6)
Networks Metrics
103(4)
Scaled Connectivity
107(1)
Clustering Coefficient
107(1)
Global Rank
108(1)
Centroid Conformity
108(1)
Topological (Inter)dependency
109(1)
Density
110(1)
Heterogeneity
110(1)
Centralization
111(1)
Conclusion
111(2)
5 Artificial Intelligence for Pattern Recognition in Sports: Classifying Actions and Performance Signatures
113(30)
Introduction
113(3)
Non-Sequence Classification
116(1)
Support Vector Machine
117(2)
Neural Network
119(5)
Sequence Classification
124(1)
Ensemble Learning
124(5)
Recurrent Neural Network
129(5)
Non-Sequence vs Sequence Classification: The Golf Putting Use Case
134(3)
Human Action Recognition in Football
137(4)
Conclusion
141(2)
6 From Classification to Prediction
143(8)
Introduction
143(2)
Convergence Analysis
145(2)
Predicting the Number of Goal Attempts and Goals Scored
147(3)
Conclusion
150(1)
7 Technology, Artificial Intelligence, and the Future of Sport and Physical Activity
151(16)
Introduction
151(2)
Artificial Intelligence Needs a Powerful Conceptualization of Performance, Learning, and Development in Sport and Physical Activity
153(1)
How Ecological Dynamics Can Help Make Sense of Big Data from AI Systems
153(2)
An Ecological Dynamics Conceptualization of Human Behaviour: Implications for Use of AI Systems in Sport
155(1)
Technology Implementation Should Drive Knowledge of the Environment in Athletes
155(1)
Knowledge about Sport Performance and Practice: The Role of AI
156(2)
What Are the Key Messages from the
Chapters of This Book?
158(3)
Looking Ahead
161(6)
References 167(28)
Index 195
Duarte Araújo is Associate Professor and Head of CIPER Interdisciplinary Centre for the Study of Human Performance and Director of the Laboratory of Expertise in Sport in the Faculty of Human Kinetics at the University of Lisbon, Portugal.

Micael Couceiro is Associate Researcher at the Institute of Systems and Robotics, Coimbra, Portugal. He is also co-founder and CEO of the company Ingeniarius.

Ludovic Seifert is Professor of Motor Control and Learning, Deputy Dean of the CETAPS Lab, and Head of the Sport Performance Analysis and Big Data Master's degree at the Faculty of Sport Sciences, University of Rouen Normandie, France.

Hugo Sarmento is Assistant Professor in the Faculty of Sport Sciences and Physical Education, University of Coimbra, Portugal.

Keith Davids is Professor of Motor Learning in the Sport and Human Performance research group, Sheffield Hallam University, UK.