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Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers

1
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
2
Fondazione Istituto Farmacologico Filippo Serpero, 20159 Milano, Italy
3
E4Sport Lab, Politecnico di Milano, 20133 Milano, Italy
4
Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, 20133 Milano, Italy
5
Dipartimento di Meccanica, Politecnico di Milano, 20129 Milano, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3094; https://doi.org/10.3390/s19143094
Received: 14 June 2019 / Revised: 8 July 2019 / Accepted: 9 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Multi-Modal Sensors for Human Behavior Monitoring)
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Abstract

Changes of directions and cutting maneuvers, including 180-degree turns, are common locomotor actions in team sports, implying high mechanical load. While the mechanics and neurophysiology of turns have been extensively studied in laboratory conditions, modern inertial measurement units allow us to monitor athletes directly on the field. In this study, we applied four supervised machine learning techniques (linear regression, support vector regression/machine, boosted decision trees and artificial neural networks) to predict turn direction, speed (before/after turn) and the related positive/negative mechanical work. Reference values were computed using an optical motion capture system. We collected data from 13 elite female soccer players performing a shuttle run test, wearing a six-axes inertial sensor at the pelvis level. A set of 18 features (predictors) were obtained from accelerometers, gyroscopes and barometer readings. Turn direction classification returned good results (accuracy > 98.4%) with all methods. Support vector regression and neural networks obtained the best performance in the estimation of positive/negative mechanical work (coefficient of determination R2 = 0.42–0.43, mean absolute error = 1.14–1.41 J) and running speed before/after the turns (R2 = 0.66–0.69, mean absolute error = 0.15–018 m/s). Although models can be extended to different angles, we showed that meaningful information on turn kinematics and energetics can be obtained from inertial units with a data-driven approach. View Full-Text
Keywords: supervised learning; changes of direction; IMU; mechanical work supervised learning; changes of direction; IMU; mechanical work
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Zago, M.; Sforza, C.; Dolci, C.; Tarabini, M.; Galli, M. Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers. Sensors 2019, 19, 3094.

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