Artificial Intelligence in Sport Performance Analysis - Artificial Intelligence in Sport Performance Analysis -

Empowering Human Intelligence: The Ecological Dynamics Approach to Big Data and Artificial Intelligence in Sport Performance Preparation Big Data in Sport Sources of Big Data Validity and Reliability of Big Data Measurements Grasping Big Data with Visual Analytics Design of Visual Analytics Systems Processing Big Data by Means of Artificial Intelligence Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning Machine Learning and Behaviour Recognition Big Data and Sport Sciences: How to Converge?Abductive Method in Sport Sciences Research From Artificial Intelligence to Empowered Human Intelligence Conceptual Problems with the Term ‘Artificial Intelligence’Human Intelligence Intelligent Sport Performance Intelligent Sport Performance Is Embodied Ecological Dynamics Approach Informs the Use of Artificial Intelligence in Sport How Is Artificial Intelligence Being Used in the Sport Sciences to Analyse and Support Performance of Athletes and Teams?AI in Sport Science: Research overview Predicting Performance Injury Prevention Pattern Recognition A Highlighted Source of Big Data for Artificial Intelligence: The Growing Impact of Automated Tracking Systems Conclusion From Reliable Sources of Big Data to Capturing Sport Performance by Ecophysical Variables Representative Assessment Design and Technology Notational Analysis from Video-Based Systems Notational Analysis Principles Validity and Reliability of Notational Analysis Examples of Studies Looking at Validity and/or Reliability of Notational Analysis D and 3D Automatic Tracking from Video and Optokinetic Multi-Camera Systems TV Broadcast Tracking Technology Multi-Video Camera Systems Optokinetic Camera Systems Sensors Smartwatch, Smartphone, and Global Positioning System (GPS)Inertial Measurement Unit (IMU)Validity, Reliability, and Accuracy of IMU Ecophysical Variables to Capture the Ecological Dynamics of Sport Performance Football Rugby Swimming Climbing Conclusion Computational Metrics to Inspect the Athletic Performance Individual Metrics Kinematic Measures Velocity Distance Orientation Trajectory Entropy Fractional Dynamics Physiologic Metrics Heart Rate Electromyography Muscle Load Electromyography Root Mean Square Electromyography Fourier Transform Group Metrics Spatial-Temporal Metrics Weighted Centroid Weighted Stretch Index Effective Surface Area Networks Metrics Scaled Connectivity Clustering Coefficient Global Rank Centroid Conformity Topological (Inter)dependency Density Heterogeneity Centralization Conclusion Artificial Intelligence for Pattern Recognition in Sports: Classifying Actions and Performance Signatures Non-Sequence Classification Support Vector Machine Neural Network Sequence Classification Ensemble Learning Recurrent Neural Network Non-Sequence vs Sequence Classification: The Golf Putting Use Case Human Action Recognition in Football Conclusion From Classification to Prediction Convergence Analysis Predicting the Number of Goal Attempts and Goals Scored Conclusion Technology, Artificial Intelligence, and the Future of Sport and Physical Activity Artificial Intelligence Needs a Powerful Conceptualization of Performance, Learning, and Development in Sport and Physical Activity How Ecological Dynamics Can Help Make Sense of Big Data from AI Systems An Ecological Dynamics Conceptualization of Human Behaviour: Implications for Use of AI Systems in Sport Technology Implementation Should Drive Knowledge of the Environment in Athletes Knowledge about Sport Performance and Practice: The Role of AI What Are the Key Messages from the Chapters of This Book?Looking Ahead