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Artificial Intelligence in Sport Performance Analysis

Empowering Human Intelligence: The Ecological Dynamics Approach to Big Data and Artificial Intelligence in Sport Performance PreparationBig Data in SportSources of Big DataValidity and Reliability of Big Data MeasurementsGrasping Big Data with Visual AnalyticsDesign of Visual Analytics SystemsProcessing Big Data by Means of Artificial IntelligenceMachine LearningSupervised Machine LearningUnsupervised Machine LearningDeep LearningMachine Learning and Behaviour RecognitionBig Data and Sport Sciences: How to Converge?Abductive Method in Sport Sciences ResearchFrom Artificial Intelligence to Empowered Human IntelligenceConceptual Problems with the Term ‘Artificial Intelligence’Human IntelligenceIntelligent Sport PerformanceIntelligent Sport Performance Is EmbodiedEcological Dynamics Approach Informs the Use of Artificial Intelligence in SportHow Is Artificial Intelligence Being Used in the Sport Sciences to Analyse and Support Performance of Athletes and Teams?AI in Sport Science: Research overviewPredicting PerformanceInjury PreventionPattern RecognitionA Highlighted Source of Big Data for Artificial Intelligence: The Growing Impact of Automated Tracking SystemsConclusionFrom Reliable Sources of Big Data to Capturing Sport Performance by Ecophysical VariablesRepresentative Assessment Design and TechnologyNotational Analysis from Video-Based SystemsNotational Analysis PrinciplesValidity and Reliability of Notational AnalysisExamples of Studies Looking at Validity and/or Reliability of Notational AnalysisD and 3D Automatic Tracking from Video and Optokinetic Multi-Camera SystemsTV Broadcast Tracking TechnologyMulti-Video Camera SystemsOptokinetic Camera SystemsSensorsSmartwatch, Smartphone, and Global Positioning System (GPS)Inertial Measurement Unit (IMU)Validity, Reliability, and Accuracy of IMUEcophysical Variables to Capture the Ecological Dynamics of Sport PerformanceFootballRugbySwimmingClimbingConclusionComputational Metrics to Inspect the Athletic PerformanceIndividual MetricsKinematic MeasuresVelocityDistanceOrientationTrajectory EntropyFractional DynamicsPhysiologic MetricsHeart RateElectromyographyMuscle LoadElectromyography Root Mean SquareElectromyography Fourier TransformGroup MetricsSpatial-Temporal MetricsWeighted CentroidWeighted Stretch IndexEffective Surface AreaNetworks MetricsScaled ConnectivityClustering CoefficientGlobal RankCentroid ConformityTopological (Inter)dependencyDensityHeterogeneityCentralizationConclusionArtificial Intelligence for Pattern Recognition in Sports: Classifying Actions and Performance SignaturesNon-Sequence ClassificationSupport Vector MachineNeural NetworkSequence ClassificationEnsemble LearningRecurrent Neural NetworkNon-Sequence vs Sequence Classification: The Golf Putting Use CaseHuman Action Recognition in FootballConclusionFrom Classification to PredictionConvergence AnalysisPredicting the Number of Goal Attempts and Goals ScoredConclusionTechnology, Artificial Intelligence, and the Future of Sport and Physical ActivityArtificial Intelligence Needs a Powerful Conceptualization of Performance, Learning, and Development in Sport and Physical ActivityHow Ecological Dynamics Can Help Make Sense of Big Data from AI SystemsAn Ecological Dynamics Conceptualization of Human Behaviour: Implications for Use of AI Systems in SportTechnology Implementation Should Drive Knowledge of the Environment in AthletesKnowledge about Sport Performance and Practice: The Role of AIWhat Are the Key Messages from the Chapters of This Book?Looking Ahead

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