Load Optimisation on Wingate Test Using Artificial Neural Networks

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dc.contributor.authorÖzbay, Yüksel
dc.date.accessioned2020-03-26T16:47:04Z
dc.date.available2020-03-26T16:47:04Z
dc.date.issued2004
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn 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 Selçuk University. By using exercises results unit load optimisation was realized using artificial neural networks.en_US
dc.identifier.citationÖzbay, Y., (2004). Load Optimisation on Wingate Test Using Artificial Neural Networks. Istanbul University Journal of Electrical and Electronics Engineering, 4(2), 1155-1159.
dc.identifier.endpage1159en_US
dc.identifier.issn1303-0914en_US
dc.identifier.issue2en_US
dc.identifier.startpage1155en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TmpFeU1qWTI=
dc.identifier.urihttps://hdl.handle.net/20.500.12395/18798
dc.identifier.volume4en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofIstanbul University Journal of Electrical and Electronics Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectMühendisliken_US
dc.subjectElektrik ve Elektroniken_US
dc.subjectWingate Test
dc.subjectPeak Power
dc.subjectMean Power
dc.subjectFatigue Index
dc.subjectANN
dc.subjectLoad Optimisation
dc.titleLoad Optimisation on Wingate Test Using Artificial Neural Networksen_US
dc.typeArticleen_US

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