Affiliations: [a] School of Sports and Physical Education, Shandong Sport University, Rizhao, Shandong, China | [b] Department of Maritime and Mechanical Engineering, Liverpool John Moores University, Liverpool, UK | [c] Department of Electronics and Electrical Engineering, Liverpool John Moores University, Liverpool, UK
Correspondence:
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Corresponding author: Qian Zhang, Department of Electronics and Electrical Engineering, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK. Tel.: +44 151 231 1396; E-mail: Q.Zhang@ljmu.ac.uk.
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 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.
Keywords: Taekwondo, leg power, training, weighted squat, data-driven modelling, fuzzy rule-based system
