Liu, J, Liu, X and Zhang, Q (2020) A new training method for leg explosive power in taekwondo and its data-driven predictive models. Isokinetics and Exercise Science. ISSN 0959-3020

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Abstract

BACKGROUND: Kicking is the major way to score in a Taekwondo competition, which makes athletes’ leg power a key quality. However, the characteristics of leg power are very complex and it is difficult to generate physical models to predict training performance. OBJECTIVE: To study training programmes of leg power for Taekwondo using data-driven techniques in correlation analyses and modelling. METHODS: An 8-week program for training back squat training was performed using two devices, a Cormax training system and a conventional barbell. Data analysis was conducted to identify the factors affecting the explosive power training. Finally, a data-driven modelling paradigm employing fuzzy rule-based systems was developed to predict the training performance. RESULTS: The Cormax system performed better in improving athletes’ maximum power and velocity. Maximum leg power was best correlated with athletes’ height. The developed predictive models showed good accuracy despite possession of limited training data. CONCLUSIONS: This study demonstrated some new training devices which could greatly improve power training. Moreover, a state-of-the-art modelling strategy was able to construct accurate models for training and exercise performance. The predictive models will likely enhance the anticipation of training outcome in advance which may assist in formulating and improving the training programmes.

Item Type:	Article
Uncontrolled Keywords:	1106 Human Movement and Sports Sciences, 0913 Mechanical Engineering
Subjects:	R Medicine > RC Internal medicine > RC1200 Sports Medicine T Technology > T Technology (General)
Divisions:	Electronics & Electrical Engineering (merged with Engineering 10 Aug 20) Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher:	IOS Press
Date Deposited:	20 Apr 2020 09:42
Last Modified:	04 Sep 2021 07:27
DOI or ID number:	10.3233/IES-202110
URI:	https://researchonline.ljmu.ac.uk/id/eprint/12761

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