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The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables

Zahari, Taha and Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed and Mohamad Razali, Abdullah and Muhammad Amirul, Abdullah and M. H. A., Hassan and Zubair, Khalil (2018) The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables. In: IOP Conference Series: Materials Science and Engineering, International Conference on Innovative Technology, Engineering and Sciences 2018 (iCITES 2018), 1-2 March 2018 , Universiti Malaysia Pahang (UMP) Pekan Campus Library, Malaysia. pp. 1-7., 342 (012020). ISSN 1757-899X

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Abstract

The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. The bio-physiological variables namely resting heart rate, resting respiratory rate, resting diastolic blood pressure, resting systolic blood pressure, as well as calories intake, were measured prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models i.e. linear, quadratic and cubic kernel functions, were trained on the aforementioned variables. The k-means clustered the archers into high (HPA) and low potential archers (LPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy with a classification accuracy of 94% in comparison the other tested models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected bio-physiological variables examined.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Blood pressure; Cluster analysis; bio-physiological variables; archers
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 28 May 2018 04:05
Last Modified: 28 May 2018 04:05
URI: http://umpir.ump.edu.my/id/eprint/21233
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