Personal fitting procedure for cycle ergometer workload control by artificial neural networks

The Internet technology was introduced to provide on-demand support for fitting the cycle ergometer workload personally depending on each individual´s physical work capacity. It was useful to combine the ratings of perceived exertion (RPE) with objective physiological indices during the exercise. We estimated the RPE from objective indices (i.e., muscular fatigue-related indices and the heart rate), using a feed-forward type artificial neural network. Interpreting the estimation errors was useful to design an appropriate workload for an individual elderly person.