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Open AccessArticle

Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning

1
Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
2
Department of Sport and Sport Science, University of Freiburg, 79117 Freiburg, Germany
3
Joint Center Black Forest, Hospital Neuenbuerg, 75305 Neuenbuerg, Germany
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3690; https://doi.org/10.3390/s19173690
Received: 4 July 2019 / Revised: 19 August 2019 / Accepted: 23 August 2019 / Published: 25 August 2019
(This article belongs to the Special Issue Inertial Sensors)
Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications. View Full-Text
Keywords: inertial sensors; artificial neural network; biomechanics; inverse dynamics inertial sensors; artificial neural network; biomechanics; inverse dynamics
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Stetter, B.J.; Ringhof, S.; Krafft, F.C.; Sell, S.; Stein, T. Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning. Sensors 2019, 19, 3690.

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