Nowadays, young athletes have injuries that leave them out the competitions, this injuries may occur due to the lack of the basic principles of weight training, muscle imbalance, fatigue, overhead, among others. The proposed weightlifting monitoring system is intended to coaches in order to provide them better control of the athletes during the training session, through continuous monitoring of exercise's measurements. This allows to detect certain risks of injury that may result if an exercise is poorly executed. To handle the uncertainty generated by the comparison of data obtained from athlete with the data of the biomechanical model for weightlifting, a set of neural networks to classify the athletes' movements during training is used. This paper describes the biomechanical model and the proposed neural network design for such monitoring system.

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La estadística de muertes por hipertermia asociadas a vehículos registra una media de 37 menores por año desde 1998 en los Estados Unidos. Se han establecido diversos motivos por los que sucedieron estos hechos, siendo el más frecuente el olvido no intencional del menor por parte del responsable. Con el fin de prevenir los lapsos de memoria se busca aprovechar los recursos tecnológicos existentes para ofrecer una solución a esta problemática. Este trabajo presenta un modelo Multi-agente enfocado en prevenir el olvido de un bebé en un automóvil utilizando el modelo de organización de la metodología INGENIAS.

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