|تعداد مشاهده مقاله||4,397,705|
|تعداد دریافت فایل اصل مقاله||1,941,953|
Postural Balance for Selection of Martial Artists Using Machine Learning Techniques
|Journal of Exercise and Health Science|
|مقاله 1، دوره 2، شماره 1، فروردین 2022، صفحه 1-12 اصل مقاله (511.26 K)|
|نوع مقاله: Original Article|
|شناسه دیجیتال (DOI): 10.22089/jehs.2022.6782.1000|
|Muhammad Manshadi* 1؛ Ehsan Ranjbar2؛ Reyhaneh Ghasab Sedehi3؛ Navid Hassani4؛ Nader Jafarnia Dabanloo5|
|1Alborz University of Medical Sciences, Karaj, Iran|
|2BSc Graduate in Biomedical Engineering. MSc Graduate in Electrical Engineering, Amirkabir University of Technology Tehran, Iran|
|3Former Biomedical Engineering Expert, Department of Medical Equipment, ABZUMS, Karaj, Iran.|
|4Medical Lab Sciences Technologist, Head of the Department of Medical Equipment, ABZUMS, Karaj, Iran.|
|5Department of Biomedical Engineering, Islamic Azad University (IAU), Science and Research Branch, Tehran, Iran|
|Objectives: The purpose of this study was to classify participants, according to balance test scores, and to detect martial art athletes.|
Design: Measures of static and dynamic balance indices were obtained from 4 tests.
Setting: This research took place at a secondary school in Iran.
Participants: Fifty healthy volunteers participated in this experiment.
Main outcome measures: Due to differences in power and different pressures applied on joints and muscles, athletes in different sports and also non-athletes may have different grades in balance tests. There isn’t enough information on specific or non-specific balance in sports.
Results: Balance test scores were used for inputs of classifiers where the applied methods included the support vector machine, k-nearest neighbors algorithm, and artificial neural network. Only by the result of 4 tests, detection accuracy of 90.5% was achieved.
Conclusion: Balance indices are good features for detection of martial art athletes. This may also be useful for talent identification in martial arts.
|Artificial neural network؛ Balance؛ Classification؛ K-nearest neighbors؛ Support vector machine|
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