Ambient intelligence systems for personalized sport training
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AuthorVales Alonso, Javier; López Matencio Pérez, Pablo Antonio; González Castaño, Francisco Javier; Navarro Hellín, Honorio; Baños Guirao, Pedro José; [et al.]
Research GroupGrupo Ingeniería Telemática (GIT)
Knowledge AreaIngeniería Telemática
SponsorsThis 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).
Bibliographic CitationVALES 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 1424-8220
Red de sensor inalámbrico
Ambient intelligence (AmI)
Wireless sensor network
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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