Format du dépôt |
Fichier |
Type de dépôt |
Article dans une revue |
Titre |
en
Prediction of lumbar fatigue during gymnastics landings using statistical modelling and Machine Learning
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Résumé |
en
Gymnastics is associated with a high rate of injuries, particularly to the back. This is due to the fact that landing involves strong ground reaction forces as well as important body rotations, hence as a result of repetitive landing, these movement patterns can lead to significant muscle fatigue, which increases the risk of injury (Campbell et al., 2019). Fatigue influences kinematics during repetitive movements such as drop jumps or create lifting by decreasing hip and knee flexion angles and increasing trunk instability through a greater shift in centre of mass (Kazemi et al., 2021). Under laboratory conditions, muscle fatigue is usually measured by EMG (Thrope et al., 2017). However, incorporating EMG measurement into everyday training is difficult, it requires a fair amount of money, proper setup time and data processing. Thus, considering that fatigue affects kinematics, motion capture seems to be another possible way to measure athlete’s fatigue. Coupling this tool with Machine Learning models would also reduce analysis time. This would further reduce the workload of the athlete.
The main objective of this study is to predict the fatigue in the lumbar region during landing repetitions only using the kinematic data of the athlete. A second objective of our research is to identify the best machine learning model for our data and the variables that would most predict fatigue during gymnastics landing. In agreement with the literature, we think that fatigue will lead to a decrease in the knee and hip flexion angles and in the displacement of centre of mass.
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Auteur(s)
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Anaïs Farr
1, 2
, Rébecca Crolan
2, 3, 4
, Mathieu Ménard
4, 5
, Charles Pontonnier
6, 3
, Diane Haering
4, 3
1
CIAMS -
Complexité, Innovation, Activités Motrices et Sportives
( 1051127 )
- Université Paris Saclay, Bâtiment 335, 91405 ORSAY
- France
-
Université d'Orléans UR 4532 ( 300297 )
;
-
Université Paris-Saclay UR 4532 ( 419361 )
2
ENS Rennes -
École normale supérieure - Rennes
( 247362 )
- Campus de Ker Lann - avenue Robert Schuman - 35170 Bruz
- France
3
MIMETIC -
Analysis-Synthesis Approach for Virtual Human Simulation
( 1092621 )
- Campus de Beaulieu 35042 Rennes cedex
- France
-
Université de Rennes 2 ( 406201 )
;
-
Inria Rennes – Bretagne Atlantique ( 419153 )
;
-
Institut National de Recherche en Informatique et en Automatique ( 300009 )
;
-
RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE ( 1092618 )
;
-
Institut de Recherche en Informatique et Systèmes Aléatoires ( 490899 )
;
-
Université de Rennes ( 105160 )
;
-
Institut National des Sciences Appliquées - Rennes ( 117606 )
;
-
Institut National des Sciences Appliquées ( 301232 )
;
-
Université de Bretagne Sud ( 172265 )
;
-
École normale supérieure - Rennes ( 247362 )
;
-
Institut National de Recherche en Informatique et en Automatique ( 300009 )
;
-
CentraleSupélec ( 411575 )
;
-
Centre National de la Recherche Scientifique UMR6074 ( 441569 )
;
-
IMT Atlantique ( 481355 )
;
-
Institut Mines-Télécom [Paris] ( 302102 )
4
M2S -
Laboratoire Mouvement Sport Santé
( 528695 )
- Université Rennes 2, UFR APS, Place du Recteur Henri Le Moal, CS 24307, 35044 RENNES CEDEX
- France
-
Université de Rennes UR7470 ( 105160 )
;
-
École normale supérieure - Rennes ( 247362 )
;
-
Université de Brest ( 300314 )
;
-
Université de Rennes 2 UR7470 ( 406201 )
;
-
Structure Fédérative de Recherche en Biologie et Santé de Rennes ( 453207 )
5
Institut d'ostéopathie de Rennes [Bruz]
( 530228 )
- Rue Blaise Pascal, 35170 Bruz
- France
6
Ecole Normale Supérieure
( 217447 )
- Congo
- Congo-Brazzaville
-
Université Marien-Ngouabi [Université de Brazzaville] = Marien Ngouabi University [University of Brazzaville] ( 302486 )
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Langue du document |
Anglais
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Nom de la revue |
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Date de publication |
2022
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Vulgarisation |
Non
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Comité de lecture |
Oui
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Audience |
Internationale
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Projet(s) ANR |
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Domaine(s) |
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Sciences du Vivant [q-bio]
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Sciences de l'ingénieur [physics]
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Mots-clés |
en
motion capture jump fatigue Random Forest force plateform, motion capture, jump, fatigue, Random Forest, force plateform
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