پژوهشگاه تربیت بدنی و علوم ورزشی

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Manshadi, Muhammad, Ranjbar, Ehsan, Ghasab Sedehi, Reyhaneh, Hassani, Navid, Jafarnia Dabanloo, Nader. (1400). Postural Balance for Selection of Martial Artists Using Machine Learning Techniques. , 2(1), 1-12. doi: 10.22089/jehs.2022.6782.1000
Muhammad Manshadi; Ehsan Ranjbar; Reyhaneh Ghasab Sedehi; Navid Hassani; Nader Jafarnia Dabanloo. "Postural Balance for Selection of Martial Artists Using Machine Learning Techniques". , 2, 1, 1400, 1-12. doi: 10.22089/jehs.2022.6782.1000
Manshadi, Muhammad, Ranjbar, Ehsan, Ghasab Sedehi, Reyhaneh, Hassani, Navid, Jafarnia Dabanloo, Nader. (1400). 'Postural Balance for Selection of Martial Artists Using Machine Learning Techniques', , 2(1), pp. 1-12. doi: 10.22089/jehs.2022.6782.1000
Manshadi, Muhammad, Ranjbar, Ehsan, Ghasab Sedehi, Reyhaneh, Hassani, Navid, Jafarnia Dabanloo, Nader. Postural Balance for Selection of Martial Artists Using Machine Learning Techniques. , 1400; 2(1): 1-12. doi: 10.22089/jehs.2022.6782.1000

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
نویسندگان
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
چکیده
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.
کلیدواژه‌ها
Artificial neural network؛ Balance؛ Classification؛ K-nearest neighbors؛ Support vector machine
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