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Article

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

1
Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland
2
School of Allied Health, Health Research Institute, University of Limerick, V94T9PX Limerick, Ireland
3
Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork, T12XF62 Cork, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3029; https://doi.org/10.3390/s20113029
Received: 1 April 2020 / Revised: 10 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred. View Full-Text
Keywords: machine learning; ACL; biomechanics; IMUs; inertial sensors; gait analysis; running; on-the-field; rugby machine learning; ACL; biomechanics; IMUs; inertial sensors; gait analysis; running; on-the-field; rugby
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MDPI and ACS Style

Tedesco, S.; Crowe, C.; Ryan, A.; Sica, M.; Scheurer, S.; Clifford, A.M.; Brown, K.N.; O’Flynn, B. Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players. Sensors 2020, 20, 3029. https://doi.org/10.3390/s20113029

AMA Style

Tedesco S, Crowe C, Ryan A, Sica M, Scheurer S, Clifford AM, Brown KN, O’Flynn B. Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players. Sensors. 2020; 20(11):3029. https://doi.org/10.3390/s20113029

Chicago/Turabian Style

Tedesco, Salvatore, Colum Crowe, Andrew Ryan, Marco Sica, Sebastian Scheurer, Amanda M. Clifford, Kenneth N. Brown, and Brendan O’Flynn. 2020. "Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players" Sensors 20, no. 11: 3029. https://doi.org/10.3390/s20113029

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