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 y...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book Section |
| Language: | English |
| Published: |
Pleiades Publishing
2018
|
| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/3723/1/FH05-FSSG-18-13761.pdf http://eprints.unisza.edu.my/3723/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Sultan Zainal Abidin |
| Language: | English |
| Summary: | 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. © 2018, Springer Nature Singapore Pte Ltd. |
|---|