Next Article in Journal
Stress Sensors and Signal Transducers in Cyanobacteria
Previous Article in Journal
Refractive Index Sensor Based on a 1D Photonic Crystal in a Microfluidic Channel
Sensors 2010, 10(3), 2359-2385; doi:10.3390/s100302359

Ambient Intelligence Systems for Personalized Sport Training

1,* , 1
, 2
, 1
, 1
, 1
, 3
, 3
, 2
 and 4
Received: 22 January 2010 / Revised: 3 March 2010 / Accepted: 10 March 2010 / Published: 22 March 2010
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [6011 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: Several research programs are tackling the use of Wireless Sensor Networks (WSN) at specific fields, such as e-Health, e-Inclusion or e-Sport. This is the case of the project “Ambient Intelligence Systems Support for Athletes with Specific Profiles”, which intends to assist athletes in their training. In this paper, the main developments and outcomes from this project are described. The architecture of the system comprises a WSN deployed in the training area which provides communication with athletes’ mobile equipments, performs location tasks, and harvests environmental data (wind speed, temperature, etc.). Athletes are equipped with a monitoring unit which obtains data from their training (pulse, speed, etc.). Besides, a decision engine combines these real-time data together with static information about the training field, and from the athlete, to direct athletes’ training to fulfill some specific goal. A prototype is presented in this work for a cross country running scenario, where the objective is to maintain the heart rate (HR) of the runner in a target range. For each track, the environmental conditions (temperature of the next track), the current athlete condition (HR), and the intrinsic difficulty of the track (slopes) influence the performance of the athlete. The decision engine, implemented by means of (m; s)-splines interpolation, estimates the future HR and selects the best track in each fork of the circuit. This method achieves a success ratio in the order of 80%. Indeed, results demonstrate that if environmental information is not take into account to derive training orders, the success ratio is reduced notably.
Keywords: ambient intelligence; contextual services; wireless sensor networks; sport training; machine learning ambient intelligence; contextual services; wireless sensor networks; sport training; machine learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |

MDPI and ACS Style

Vales-Alonso, J.; López-Matencio, P.; Gonzalez-Castaño, F.J.; Navarro-Hellín, H.; Baños-Guirao, P.J.; Pérez-Martínez, F.J.; Martínez-Álvarez, R.P.; González-Jiménez, D.; Gil-Castiñeira, F.; Duro-Fernández, R. Ambient Intelligence Systems for Personalized Sport Training. Sensors 2010, 10, 2359-2385.

AMA Style

Vales-Alonso J, López-Matencio P, Gonzalez-Castaño FJ, Navarro-Hellín H, Baños-Guirao PJ, Pérez-Martínez FJ, Martínez-Álvarez RP, González-Jiménez D, Gil-Castiñeira F, Duro-Fernández R. Ambient Intelligence Systems for Personalized Sport Training. Sensors. 2010; 10(3):2359-2385.

Chicago/Turabian Style

Vales-Alonso, Javier; López-Matencio, Pablo; Gonzalez-Castaño, Francisco J.; Navarro-Hellín, Honorio; Baños-Guirao, Pedro J.; Pérez-Martínez, Francisco J.; Martínez-Álvarez, Rafael P.; González-Jiménez, Daniel; Gil-Castiñeira, Felipe; Duro-Fernández, Richard. 2010. "Ambient Intelligence Systems for Personalized Sport Training." Sensors 10, no. 3: 2359-2385.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert