LOAD OPTIMISATION ON WINGATE TEST USING ARTIFICIAL NEURAL NETWORKS

In supplying the power loss for a period of time that came out during the physical activities, available energy metabolism on leg muscles plays a very important role. In Wingate Test (WT) that's developed for the anaerobic power measurement on leg muscles, a person is demanded to pedal a special bicycle for 30 seconds under a determined load. At first, a unit load (gr/kg) is determined and a friction force, which is proportional to the person's weight, is applied to the pedal-strap. The friction force that'll be applied to bicycle's pedal-strap must be determined. The determination of the unit load values depends on persons' age, weight, sex, and condition of fitness. Wingate anaerobic test was performed on 35 volunteered and untrained male medical students (mean age 21.3 ± 2.1, mean length 172.1 ± 6.3 cm, mean weight 73.5 ± 8.4 kg) at Physiology Department of Medicine Faculty of Selcuk University. By using exercises results unit load optimisation was realized using artificial neural networks.

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