Functional Data Analysis in Biomechanics: A Concise Review of Core Techniques, Applications and Emerging Areas - Couverture souple

Gunning, Edward; Warmenhoven, John; Harrison, Andrew J.; Bargary, Norma

Couverture souple

ISBN 10 : 3031688619 ISBN 13 : 9783031688614

Editeur : Springer, 2024

Synopsis

This book provides a concise discussion of fundamental functional data analysis (FDA) techniques for analysing biomechanical data, along with an up-to-date review of their applications. The core of the book covers smoothing, registration, visualisation, functional principal components analysis and functional regression, framed in the context of the challenges posed by biomechanical data and accompanied by an extensive case study and reproducible examples using R. This book proposes future directions based on recently published methodological advancements in FDA and emerging sources of data in biomechanics. This is a vibrant research area, at the intersection of applied statistics, or more generally, data science, and biomechanics and human movement research. This book serves as both a contextual literature review of FDA applications in biomechanics and as an introduction to FDA techniques for applied researchers. In particular, it provides a valuable resource for biomechanics researchers seeking to broaden or deepen their FDA knowledge.

A propos du livre

À propos de l'auteur

Edward Gunning is a Postdoctoral Researcher in Biostatistics at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA. He obtained his PhD in Statistics at the Science Foundation Ireland Centre for Research Training in Foundations of Data Science, based in the University of Limerick. His PhD research concerned the application and development of techniques to analyse multivariate functional data that arise in sports biomechanics. His current work is focused on the development of new functional data analysis methodology for biomedical research.

John Warmenhoven is a Lecturer in Sports Data-Science at the School of Sport, Exercise and Rehabilitation, University of Technology Sydney (UTS), in Australia. He was previously a research fellow in sports meta-science at UTS, and also a Data Science Fellow at the University of New South Wales in Canberra. He has worked across both biomechanics and data-science domains, and was a previous recipient of the ISBS New Investigator Award and Hans Gros Emerging Researcher Award, both received for integrating Functional Data Analysis methods into applied sports biomechanics research.

Andrew (Drew) J. Harrison is an Emeritus Professor of sport and exercise biomechanics in the Department of Physical Education & Sport Sciences at University of Limerick, Ireland. He established and co-directed the Sport and Human Performance Research Centre and the Biomechanics Research Unit at the University of Limerick. He was President of the International Society of Biomechanics in Sport (ISBS) and a recipient of the ISBS Geoffrey Dyson Award in 2014. He has over 20 years of experience in applying functional data analysis techniques in sport and exercise biomechanics research.

Norma Bargary is a Professor of Data Science & Statistical Learning at the Department of Mathematics & Statistics and Director of the Mathematics Applications Consortium for Science and Industry (MACSI) Research Centre, University of Limerick, Ireland. Her primary area of expertise is developing new statistical approaches for modelling functional data from complex systems. In particular, she has 20 years of experience collaborating with biomechanics and sports science researchers and practitioners, developing and applying functional data analysis methods in these fields. She also leads statistical research programmes across Centres of Excellence such as Insight Centre for Data Analytics, the Solid State Pharmaceutical Centre, and Confirm Centre for Smart Manufacturing.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2024
• Langue: anglais
• ISBN 10: 3031688619
• ISBN 13: 9783031688614
• Reliure: Broché
• Nombre de pages: 100
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