Hesitant Bipolar Fuzzy MCDM Framework for Evaluating Swimming Analysis Technologies - Liang, Xiulu (Informatica, 2025)
The analysis of swimming techniques has become increasingly significant for enhancing performance metrics and optimizing training methods. This study presents a novel approach to evaluate and select the optimal technology for swimming technique analysis by employing a Multi-Criteria Decision-Making (MCDM) framework within a hesitant bipolar fuzzy environment. Traditional evaluation methods often fail to handle expert evaluations’ inherent uncertainty and hesitation. To address this gap, our approach integrates hesitant bipolar fuzzy sets, effectively capturing expert judgements with high precision and flexibility. Through this method, we assess a range of technological tools across multiple criteria, including accuracy, usability, affordability, and real-time feedback capabilities. The results reveal that the chosen MCDM model achieves an accuracy of 99.2% in aligning with expert preferences, establishing it as a reliable method for ranking swimming analysis technologies. Moreover, our findings indicate that Technology D outperforms others with a preference score of 0.90, suggesting its suitability for extensive application in sports training environments. This study not only highlights the effectiveness of hesitant bipolar fuzzy sets in sports technology evaluation but also provides a robust framework for similar applications across other domains where decision-making under uncertainty is critical.