Comparison of machine learning classifiers for differentiating level and sport using movement data. Issue 19 (2nd October 2022)

• Record Type: Journal Article
• Authors: Ross, Gwyneth B.; Clouthier, Allison L.; Boyle, Alistair; Fischer, Steven L.; Graham, Ryan B.
• Is Part Of: Journal of sports sciences. Volume 40:Issue 19(2022)
• Page Start: 2166
• Page End: 2172
• Publication Date: 2022-10-02
• Subjects: Recurrent neural networks -- time-series -- movement screens -- reservoir computing -- long short-term memory; Sports -- Periodicals; Sports -- Physiological aspects -- Periodicals; Sports -- Psychological aspects -- Periodicals; 612.044
• DOI: 10.1080/02640414.2022.2145430
• Languages: English
• ISSNs: 0264-0414

Abstract

ABSTRACT: The purposes of this study were to determine if 1) recurrent neural networks designed for multivariate, time-series analyses outperform traditional linear and non-linear machine learning classifiers when classifying athletes based on competition level and sport played, and 2) athletes of different sports move differently during non-sport-specific movement screens. Optical-based kinematic data from 542 athletes were used as input data for nine different machine learning algorithms to classify athletes based on competition level and sport played. For the traditional machine learning classifiers, principal component analysis and feature selection were used to reduce the data dimensionality and to determine the best principal components to retain. Across tasks, recurrent neural networks and linear machine learning classifiers tended to outperform the non-linear machine learning classifiers. For all tasks, reservoir computing took the least amount of time to train. Across tasks, reservoir computing had one of the highest classification rates and took the least amount of time to train; however, interpreting the results is more difficult compared to linear classifiers. In addition, athletes were successfully classified based on sport suggesting that athletes competing in different sports move differently during non-sport specific movements. Therefore, movement assessment screens should incorporate sport-specific scoring criteria.