Literature DB >> 23898682

Movement variability and skills monitoring in sports.

Ezio Preatoni1, Joseph Hamill, Andrew J Harrison, Kevin Hayes, Richard E A Van Emmerik, Cassie Wilson, Renato Rodano.   

Abstract

The aim of this paper was to present a review on the role that movement variability (MV) plays in the analysis of sports movement and in the monitoring of the athlete's skills. MV has been traditionally considered an unwanted noise to be reduced, but recent studies have re-evaluated its role and have tried to understand whether it may contain important information about the neuro-musculo-skeletal organisation. Issues concerning both views of MV, different approaches for analysing it and future perspectives are discussed. Information regarding the nature of the MV is vital in the analysis of sports movements/motor skills, and the way in which these movements are analysed and the MV subsequently quantified is dependent on the movement in question and the issues the researcher is trying to address. In dealing with a number of issues regarding MV, this paper has also raised a number of questions which are still to be addressed.

Mesh:

Year:  2013        PMID: 23898682     DOI: 10.1080/14763141.2012.738700

Source DB:  PubMed          Journal:  Sports Biomech        ISSN: 1476-3141            Impact factor:   2.832


  21 in total

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Journal:  Ann Biomed Eng       Date:  2017-09-25       Impact factor: 3.934

3.  Preliminary Feasibility Study to Measure the Immediate Changes of Bilateral Asymmetry After Lumbar Spinal Manipulative Therapy in Asymptomatic Athletes.

Authors:  Bruno Alvarenga; Marcelo Botelho; Jerusa Lara; Filipa João; António Veloso
Journal:  J Chiropr Med       Date:  2020-08-21

4.  A Proposed Framework to Describe Movement Variability within Sporting Tasks: A Scoping Review.

Authors:  Jake Cowin; Sophia Nimphius; James Fell; Peter Culhane; Matthew Schmidt
Journal:  Sports Med Open       Date:  2022-06-27

5.  Analytical-stochastic model of motor difficulty for three-dimensional manipulation tasks.

Authors:  Andrea Lucchese; Salvatore Digiesi; Carlotta Mummolo
Journal:  PLoS One       Date:  2022-10-19       Impact factor: 3.752

6.  Analysis Choices Impact Movement Evaluation: A Multi-Aspect Inferential Method Applied to Kinematic Curves of Vertical Hops in Knee-Injured and Asymptomatic Persons.

Authors:  Johan Strandberg; Alessia Pini; Charlotte K Häger; Lina Schelin
Journal:  Front Bioeng Biotechnol       Date:  2021-05-14

Review 7.  Training for Muscular Strength: Methods for Monitoring and Adjusting Training Intensity.

Authors:  Timothy J Suchomel; Sophia Nimphius; Christopher R Bellon; W Guy Hornsby; Michael H Stone
Journal:  Sports Med       Date:  2021-06-08       Impact factor: 11.928

8.  Effective Swimmer's Action during the Grab Start Technique.

Authors:  Luis Mourão; Karla de Jesus; Hélio Roesler; Leandro J Machado; Ricardo J Fernandes; João Paulo Vilas-Boas; Mário A P Vaz
Journal:  PLoS One       Date:  2015-05-15       Impact factor: 3.240

9.  Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

Authors:  Fabian Horst; Alexander Eekhoff; Karl M Newell; Wolfgang I Schöllhorn
Journal:  PLoS One       Date:  2017-06-15       Impact factor: 3.240

10.  Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout.

Authors:  Ezio Preatoni; Stefano Nodari; Nicola Francesco Lopomo
Journal:  Front Bioeng Biotechnol       Date:  2020-07-07
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