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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.
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.
This book examines the bottom-up applicability of swarm
intelligence to solving multiple problems, such as curve fitting,
image segmentation, and swarm robotics. It compares the
capabilities of some of the better-known bio-inspired optimization
approaches, especially Particle Swarm Optimization (PSO), Darwinian
Particle Swarm Optimization (DPSO) and the recently proposed
Fractional Order Darwinian Particle Swarm Optimization (FODPSO),
and comprehensively discusses their advantages and disadvantages.
Further, it demonstrates the superiority and key advantages of
using the FODPSO algorithm, such as its ability to provide an
improved convergence towards a solution, while avoiding
sub-optimality. This book offers a valuable resource for
researchers in the fields of robotics, sports science, pattern
recognition and machine learning, as well as for students of
electrical engineering and computer science.
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