Enhanced methods in computer security, biometric and artificial intelligence systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
A context-aware service platform to support continuous care networks for home-based assistance
UAHCI'07 Proceedings of the 4th international conference on Universal access in human-computer interaction: ambient interaction
The study on chaotic anti-control of heart beat BVP system
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
A soft computing method for detecting lifetime building thermal insulation failures
Integrated Computer-Aided Engineering
Neural visualization of network traffic data for intrusion detection
Applied Soft Computing
Evolutionary selection of hyperrectangles in nested generalized exemplar learning
Applied Soft Computing
A computational model for the effect of dopamine on action selection during stroop test
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A basis for cognitive machines
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Bayesian nonlinear model selection and neural networks: a conjugate prior approach
IEEE Transactions on Neural Networks
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Current medical tendencies in the rehabilitation field are trying to physically rehabilitate patients. Thus, people with cardiovascular illnesses need to exercise their injured systems in order to improve themselves. In training, each person has a different heart rate response according to the demand of physical effort. Hence, it is necessary to know the relationship between the effort (training device power/resistance) and the patient's heartbeat for an optimal training configuration. This relationship has non-linear and complex dynamics, being a complicated identification problem solved by classical techniques. Soft Computing techniques based on artificial neural networks may be a way to implement more efficient control strategies in order to obtain a suitable power demand each and every time. It is necessary to be aware of the pace, length and intensity of the exercises in order to be effective and safe. In this paper, we present the results of the identification of the relationship in time, between the required exercise (machine resistance) and the heart rate of the patient in medical effort tests, using a NARX neural network model. In the experimental stage, test data have been obtained by exercising with a cyclo-ergometer in two different tests: Power Step Response (PSR) and Conconi.