Document Type : Original Article
Authors
Abstract
Background and Aim: Wingate test involves a 30 seconds maximal exercise on cycling ergometer while the load is selected according to the body weight. Beside the body weight, the load is also related to other parameters such as age and gender. Also other parameters such as level of fitness, body mass index smoking of the exercisers can be affecting in the load selection for the test. This study suggests an intelligent load selection method for Wingate test using artificial neural networks according to all the affective variables. Materials and Methods: In this study 30 male students of Isfahan university volunteered to perform Wingate anaerobic power test on cycling ergometer (Monark 894). Moreover, the Rapid miner software was used for prediction of optimal workload according to characteristic of subjects. Results: According to the data mining algorithms, the Height, weight, age, exercise, pelvic fat and abdominal fat indicated the greatest impact on prediction of optimal workload:. Conclusion: According to the results, neural network was able to predict the amount of load for both train and test data with %93 and %90 confidence limits, respectively. This network can be used to delicately determine the load for anyone.
Keywords
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