Rabiu Muazu Musa

The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme.

Psycho-Fitness Parameters in the Identification of High-Potential Archers

This chapter highlights the relationship of psycho-fitness variables towards the recognition of HPA and LPA. The variables, namely hand grip, upper muscle strength, core muscle strength, static balance, vertical jump and athletic coping skills, were shown to be able to provide a reasonable clustering of the archers. Moreover, it was established that the coarse variation of the k-NN model yields the better classification of the HPA and LPA.

Psychological Variables in Ascertaining Potential Archers

This chapter evaluates the relationship of athletic psychological coping skills with the archers’ performance. It was demonstrated from the study that the possession of certain psychological coping skills, namely coping with adversity, coachability, peaking under pressure, concentration, confidence and achievement motivation, goal setting and mental preparation is non-trivial in the achievement of high archery shooting score. It was also shown that the tansig-based ANN model could provide the better classification of HPA and LPA.

Anthropometry Correlation Towards Archery Performance

This chapter assesses the association of a selection of anthropometry parameters in identifying HPA and LPA. It was shown that the selected parameters, i.e. abdominal circumference, arm span, calf circumference, height, hip circumference, thigh circumference and weight, are essential in the identification of potential of the archers. Furthermore, it was demonstrated that the linear SVM variation model provided the best prediction capability amongst the other evaluated SVM models.

Machine Learning in Sports: Identifying Potential Archers

This brief highlights the association of different performance variables that influences archery performance and the employment of different machine learning algorithms in the identification of potential archers. The sport of archery is often associated with a myriad of performance indicators namely bio-physiological, psychological, anthropometric as well as physical fitness. Traditionally, the determination of potential archers is carried out by means of conventional statistical techniques. Nonetheless, such methods often fall short in associating non-linear relationships between the variables. This book explores the notion of machine learning that is capable of mitigating the aforesaid issue. This book is valuable for coaches and managers in identifying potential archers during talent identification programs.​

Bio-Physiological Indicators in Evaluating Archery Performance

The present chapter measured the association of archery performance in relation to bio-physiological indicators, namely calories intake, resting respiration, heart rate, systolic as well as diastolic blood. It was established from the investigation that a high calorie intake, resting respiratory rate and a lower level of heart rate in conjunction with lower blood pressure are positively linked with a higher archery shooting score. Moreover, it is shown from the present investigation that the LR model could provide a good prediction of LPA and HPA based on the performance indicators examined.

The application of support vector machine in classifying potential archers using bio-mechanical indicators

This study classifies potential archers from a set of bio-mechanical indicators trained via different Support Vector Machine (SVM) models. 50 youth archers drawn from a number of archery programmes completed a one end archery shooting score test. Bio-mechanical evaluation of postural sway, bow movement, muscles activation of flexor and extensor as well as static balance were recorded. k-means clustering technique was used to cluster the archers based on the indicators tested. Fine, medium and coarse radial basis function kernel-based SVM models were trained based on the measured indicators. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the employment of SVM is able to assist coaches in identifying potential athletes in the sport of archery.

Talent identification of potential archers through fitness and motor ability performance variables by means of artificial neural network

The utilisation of artificial intelligence for prediction and classification in the sport of archery is still in its infancy. The present study classified and predicted high and low potential archers from a set of fitness and motor ability variables trained on artificial neural network (ANN). 50 youth archers with the mean age and standard deviation of (17.00 ± 0.56) drawn from various archery programmes completed a one end archery shooting score test. Standard fitness and ability measurements of hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle were conducted. The cluster analysis was used to cluster the archers based on the performance variables tested to high performing archers (HPA) and low performing archers (LPA), respectively. ANN was used to train the measured performance variables. The five-fold cross-validation technique was utilised in the study. It was established that the ANN model is able to demonstrate a reasonably excellent classification on the evaluated indicators with a classification accuracy of 94% in classifying the HPA and the LPA.

Classification of High Performance Archers by Means of Bio-physiological Performance Variables via k -Nearest Neighbour Classification Model

The present study classified and predicted high and low potential archers from a set of bio-physiological variables trained via a machine learning technique namely k-Nearest Neighbour (k-NN). 50 youth archers drawn from various archery programmes completed a one end archery shooting score test. Bio-physiological measurements of systolic blood pressure, diastolic blood pressure, resting respiratory rate, resting heart rate and dietary intake were taken. Multiherachical agglomerative cluster analysis was used to cluster the archers based on the variables tested into low, medium and high potential archers. Three different k-NN models namely fine, medium and coarse were trained based on the measured variables. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the utilisation of k-NN is non-trivial in the classification of the performance of the archers.

The Identification of hunger behaviour of lates calcarifer using k-nearest neighbour

Fish Hunger behaviour is essential in determining the fish feeding routine, particularly for fish farmers. The inability to provide accurate feeding routines (under-feeding or over-feeding) may lead to the death of the fish and consequently inhibits the quantity of the fish produced. Moreover, the excessive food that is not consumed by the fish will be dissolved in the water and accordingly reduce the water quality through the reduction of oxygen quantity. This problem also leads to the death of the fish or even spur fish diseases. In the present study, a correlation of Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique is established. The behaviour is clustered with respect to the position of the school size as well as the school density of the fish before feeding, during feeding and after feeding. The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. Three different variations of the algorithm namely, fine, medium and coarse are assessed on its ability to classify the aforementioned fish hunger behaviour. It was found from the study that the fine k-NN variation provides the best classification with an accuracy of 88%. Therefore, it could be concluded that the proposed integration technique may assist fish farmers in ascertaining fish feeding routine.