科研之友
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摘要
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.
- 出版日期2013-6-3
