Validity of a microsensor-based algorithm for detecting scrum events in rugby union
Article
Article Title | Validity of a microsensor-based algorithm for detecting scrum events in rugby union |
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ERA Journal ID | 40358 |
Article Category | Article |
Authors | Chambers, Ryan M. (Author), Gabbett, Tim J. (Author) and Cole, Michael H. (Author) |
Journal Title | International Journal of Sports Physiology and Performance |
Journal Citation | 14 (2), pp. 176-182 |
Number of Pages | 7 |
Year | 2019 |
Publisher | Human Kinetics Publishers |
Place of Publication | United States |
ISSN | 1555-0265 |
1555-0273 | |
Digital Object Identifier (DOI) | https://doi.org/10.1123/ijspp.2018-0222 |
Web Address (URL) | https://journals.humankinetics.com/doi/10.1123/ijspp.2018-0222 |
Abstract | PURPOSE: Commercially available microtechnology devices containing accelerometers, gyroscopes, magnetometers, and global positioning technology have been widely used to quantify the demands of rugby union. This study investigated whether data derived from wearable microsensors can be used to develop an algorithm that automatically detects scrum events in rugby union training and match play. METHODS: Data were collected from 30 elite rugby players wearing a Catapult OptimEye S5 (Catapult Sports, Melbourne, Australia) microtechnology device during a series of competitive matches (n = 46) and training sessions (n = 51). A total of 97 files were required to 'train' an algorithm to automatically detect scrum events using random forest machine learning. A further 310 files from training (n = 167) and match-play (n = 143) sessions were used to validate the algorithm's performance. RESULTS: Across all positions (front row, second row, and back row), the algorithm demonstrated good sensitivity (91%) and specificity (91%) for training and match-play events when the confidence level of the random forest was set to 50%. Generally, the algorithm had better accuracy for match-play events (93.6%) than for training events (87.6%). CONCLUSIONS: The scrum algorithm was able to accurately detect scrum events for front-row, second-row, and back-row positions. However, for optimal results, practitioners are advised to use the recommended confidence level for each position to limit false positives. Scrum algorithm detection was better with scrums involving >/=5 players and is therefore unlikely to be suitable for scrums involving 3 players (eg, rugby sevens). Additional contact- and collision-detection algorithms are required to fully quantify rugby union demands. |
Keywords | contact detection, machine learning, microtechnology, team sport |
ANZSRC Field of Research 2020 | 420799. Sports science and exercise not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Welsh Rugby Union, United Kingdom |
Institute for Resilient Regions | |
Australian Catholic University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q53vv/validity-of-a-microsensor-based-algorithm-for-detecting-scrum-events-in-rugby-union
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