Ambient intelligence systems for personalized sport training
Autor
Vales Alonso, Javier; López Matencio Pérez, Pablo Antonio; González Castaño, Francisco Javier; Navarro Hellín, Honorio; Baños Guirao, Pedro José; [et al.]Grupo de investigación
Grupo Ingeniería Telemática (GIT)Área de conocimiento
Ingeniería TelemáticaPatrocinadores
This work has been supported by grants DEP2006-56158-C03-01/02/03, TEC2007-67966 -01/02/TCM CON-PARTE-1/2 (Ministerio de Educacion y Ciencia, Spain), TSI-020301-2008-16 ELISA and TSI-020301-2008-2 PIRAmIDE (Ministerio de Industria, Turismo y Comercio, Spain), and it has been also developed within the framework of “Programa de Ayudas a Grupos de Excelencia de la Region de Murcia”, funded by Fundacion Seneca, Agencia de Ciencia y Tecnologia de la Region de Murcia (Plan Regional de Ciencia y Tecnologia 2007/2010).Fecha de publicación
2010-03-22Editorial
MDPI AGCita bibliográfica
VALES ALONSO, Javier, LÓPEZ MATENCIO, Pablo, GONZÁLEZ CASTAÑO. Francisco, NAVARRO HELLÍN, Honorio, BAÑOS GUIRAO, Pedro J., PÉREZ MARTÍNEZ, Francisco J., MARTÍNEZ ÁLVAREZ, Rafael, P., GONZÁLEZ JIMÉNEZ, Daniel, GIL CASTIÑEIRA, Daniel, DURO FERNÁNDEZ, Richard. Ambient intelligence systems for personalized sport training. Sensors, 10: 2359-2385, Marzo 2010. ISSN 1424-8220Revisión por pares
siPalabras clave
Inteligencia ambientalServicios contextuales
Red de sensor inalámbrico
Entrenamiento deportivo
Aprendizaje automático
Ambient intelligence (AmI)
Contextual services
Wireless sensor network
Sport training
Machine learning
Resumen
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, ...
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