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Winter school : Collective behaviours and sport performance analysis



Sport performance analysis in competition and training is more and more monitored thanks to recent research in data sciences and technological development for motion capture such as GPS, inertial measurement unit, and multi-camera system. There is a real need to provide methods, tools and skills to correctly use these new devices, as well as to approach performance analysis and collective behaviours following a sound theoretical framework. Indeed, the risk of collecting multi-sourcing data is to shift toward data-driven approach rather than following theoretical framework supported by key concepts, methodology, feedback to coaches and pedagogy for training. This Winter and Summer school roots in the Complexity Sciences and intertwine various disciplines such as physics and mathematics through dynamical system theory, ecological psychology, complex system approach of neurobiology, interaction network in computer sciences, etc. The complexity sciences recently showed a growing interest to understand and to explain the individual and collective behaviours in various contexts (competitive vs. collaborative, routine vs. perturbation), especially in sport (Davids et al., 2014) and will be the heart of this Winter and Summer school.

OBJECTIVES, AIMS AND ACQUIRED SKILLS
In the Winter school, we will focus our attention on multi-sourcing data collection and analysis by simulating a building evacuation during a laser game (https://www.dockslaser.com) in various scenarios (see below for more details). The objectives are to acquire:

- skills in collecting multi-sourcing data like kinematics (from accelerometer and GPS),physiological data (heart rate), psychological data (anxiety state).

- skills in data visualisation and signal processing in order to intertwine data of various nature.

- skills in determining dependant variables to assess individual and collective behaviours, and network interactions in order to analyse sport performance outcome accordant to the task goal.

CONTENT AND NECESSARY PRIOR KNOWLEDGE
Prior knowledge necessary in the relevant field :
- Basic skill in programming ( Python, Matlab, R), signal and image processing, data vizualisation
- Skills in using motion capture systems such as multicamera system, GPS, accelerometer, inertial measurement unit, in order to track athletes kinematics.
- Basic skill in data mining, unsupervised machine learning, statistics


 


Informations pratiques :

Date de l'évènement : du 12 novembre 2018 au 16 novembre 2018
Lieu(x) : Site de Mont Saint Aignan
Plan campus

Publié le 10 septembre 2018

mise à jour le 11 septembre 2018



Winter school program : Collective behaviours and sport performance analysis
- English level : B2
- Level : Master
- Number of hours: 50
- Number of ECTS Credits : 6 ( a certificate will be provides to all attendes)
- Fees : 990 euros included lessons, housing and lunch from Monday to Friday
- Deadline : 5th October 2018


More information and registration :
short-programmes@univ-rouen.fr
- Application Form



University of Rouen Normandy





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