TY - JOUR
T1 - The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables
AU - Taha, Zahari
AU - Musa, Rabiu Muazu
AU - Abdul Majeed, Anwar P.P.
AU - Abdullah, Mohamad Razali
AU - Abdullah, Muhammad Amirul
AU - Hassan, Mohd Hasnun Arif
AU - Khalil, Zubair
N1 - Funding Information:
This work is funded by the National Sports Institute of Malaysia (ISNRG: 8/2014-12/2014) and the publication of the manuscript is funded by Universiti Malaysia Pahang (RDU 1703251)
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/4/6
Y1 - 2018/4/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85046267983&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/342/1/012020
DO - 10.1088/1757-899X/342/1/012020
M3 - Conference article
AN - SCOPUS:85046267983
SN - 1757-8981
VL - 342
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012020
T2 - International Conference on Innovative Technology, Engineering and Sciences 2018, iCITES 2018
Y2 - 1 March 2018 through 2 March 2018
ER -