Nonlinear Analysis for Human Movement Variability

This book will be an ideal purchase for any student engineer interested in understanding human movement variability including those in the fields of biomedical, industrial, and mechanical engineering.

Human movement variability can be defined as the typical variations that are present in motor performance and are observed across multiple repetitions of a task. This variability is inherent within all biological systems. It can also be observed easily as it is almost impossible for an individual to perform, even an elite performer, two identical actions of the same task. Nonlinear Analysis for Human Movement Variability, Second edition presents a different approach to studying human movement variability, based on the temporal structure of variability, that is how movement changes over time despite repetitive tasks. Featuring nine new chapters and numerous case studies, this second edition investigates human movement patterns and how these impact our daily lives. The new edition goes beyond the basics, while maintaining a simple and approachable style and having been fully updated with the latest findings from the research, focusing heavily on methodological developments and inclusion of examples from literature. New topics covered include recurrence quantification analysis, multifractal analysis, fractal correlation and regression, and multiscale probability distribution functions plus much more. New MATLAB code and datasets will accompany each chapter to facilitate teaching and learning. The reader will gather a thorough and detailed analysis and evaluation of how to measure human movement variability. This book will be an ideal purchase for any student interested in understanding human movement variability including those in fields such as biomechanics and kinesiology, along with students from biomedical, industrial, and mechanical engineering. Its appeal will extend to ergonomists, clinicians, practitioners, and physiologists too. This title also features PowerPoint slides for instructors to use in their classroom to enhance learning, alongside code and data.