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Article

Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units

1
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
2
School of Sport and Exercise Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
3
The Celtic Football Club, Celtic Park, Glasgow G40 3RE, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Giuseppe Vannozzi
Sensors 2021, 21(14), 4625; https://doi.org/10.3390/s21144625
Received: 28 May 2021 / Revised: 25 June 2021 / Accepted: 1 July 2021 / Published: 6 July 2021
(This article belongs to the Special Issue Sensors in Sports Biomechanics)
Changes of direction (COD) are an important aspect of soccer match play. Understanding the physiological and biomechanical demands on players in games allows sports scientists to effectively train and rehabilitate soccer players. COD are conventionally recorded using manually annotated time-motion video analysis which is highly time consuming, so more time-efficient approaches are required. The aim was to develop an automated classification model based on multi-sensor player tracking device data to detect COD > 45°. Video analysis data and individual multi-sensor player tracking data (GPS, accelerometer, gyroscopic) for 23 academy-level soccer players were used. A novel ‘GPS-COD Angle’ variable was developed and used in model training; along with 24 GPS-derived, gyroscope and accelerometer variables. Video annotation was the ground truth indicator of occurrence of COD > 45°. The random forest classifier using the full set of features demonstrated the highest accuracy (AUROC = 0.957, 95% CI = 0.956–0.958, Sensitivity = 0.941, Specificity = 0.772. To balance sensitivity and specificity, model parameters were optimised resulting in a value of 0.889 for both metrics. Similarly high levels of accuracy were observed for random forest models trained using a reduced set of features, accelerometer-derived variables only, and gyroscope-derived variables only. These results point to the potential effectiveness of the novel methodology implemented in automatically identifying COD in soccer players. View Full-Text
Keywords: change of direction movements; Global Positioning System; accelerometer; gyroscope; machine learning; random forest; logistic model tree change of direction movements; Global Positioning System; accelerometer; gyroscope; machine learning; random forest; logistic model tree
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MDPI and ACS Style

Reilly, B.; Morgan, O.; Czanner, G.; Robinson, M.A. Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units. Sensors 2021, 21, 4625. https://doi.org/10.3390/s21144625

AMA Style

Reilly B, Morgan O, Czanner G, Robinson MA. Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units. Sensors. 2021; 21(14):4625. https://doi.org/10.3390/s21144625

Chicago/Turabian Style

Reilly, Brian, Oliver Morgan, Gabriela Czanner, and Mark A. Robinson 2021. "Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units" Sensors 21, no. 14: 4625. https://doi.org/10.3390/s21144625

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