Evaluation of Team-Sport Training Effort Control Systems

This work studies different analytical systems to evaluate and control effort in team-sport training. They analyze real-time training data obtained by means of biometric belts and provide instructions to direct athletes’ training. The decision techniques estimate the ratios of each effort regime, based on three different methodologies: (i) best-fit polynomial approximations, (ii) Kalman filters and (iii) sliding-window distribution estimation. The goal is to predict the future effort regimes and to provide suitable training orders to control that effort. The complete system results in a virtual coach, operating in real time and automatically. This methodology has been piloted in an experiment with the UCAM Volleyball Murcia team, top of the Spanish national women’s volleyball league. Data obtained during training sessions have provided a knowledge base for the algorithms developed and allowed us to validate results.

[1]  Hassan Ghasemzadeh,et al.  Sport training using body sensor networks: a statistical approach to measure wrist rotation for golf swing , 2009, BODYNETS.

[2]  James Fogarty,et al.  iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones , 2008 .

[3]  Richard J. Duro,et al.  Ambient Intelligence Systems for Personalized Sport Training , 2010, Sensors.

[4]  Pedro Pérez Soriano,et al.  La instrumentación en la biométrica deportiva , 2007 .

[5]  Florian Michahelles,et al.  Sensing and monitoring professional skiers , 2005, IEEE Pervasive Computing.

[6]  D. K. Arvind,et al.  The speckled golfer , 2008, BODYNETS.

[7]  Arvind Sharma Physical and Physiological profiles of different levels of net ball players , 2006 .

[8]  Javier Vales-Alonso,et al.  An effort control system for training elite team-sport athletes , 2013, 2013 6th International Conference on Human System Interactions (HSI).

[9]  F. J. Gonzalez-Castano,et al.  Ambient intelligence assistant for running sports based on k-NN classifiers , 2010, 3rd International Conference on Human System Interaction.

[10]  S Armstrong,et al.  Wireless connectivity for health and sports monitoring: a review , 2007, British Journal of Sports Medicine.

[11]  Jan O. Borchers,et al.  Real-time snowboard training system , 2008, CHI Extended Abstracts.

[12]  Dennis Pfisterer,et al.  MarathonNet: adding value to large scale sport events - a connectivity analysis , 2006, InterSense '06.

[13]  Ed H. Chi,et al.  Introducing wearable force sensors in martial arts , 2005, IEEE Pervasive Computing.