Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques

Moore, Stephanie R.; Kranzinger, Christina; Fritz, Julian; Stӧggl, Thomas; Krӧll, Josef; Schwameder, Hermann

doi:10.3390/s20236737

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Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques

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Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein/Rif, Austria

Salzburg Research Forschungsgesellschaft m.b.H., Jakob-Haringer-Straße 5, 5020 Salzburg, Austria

Adidas AG, Adi-Dassler-Strasse 1, 91074 Herzogenaurach, Germany

⁴

Athlete Performance Center, Red Bull Sports, Brunnbachweg 71, 5303 Thalgau, Austria

Author to whom correspondence should be addressed.

Sensors 2020, 20(23), 6737; https://doi.org/10.3390/s20236737

Received: 12 October 2020 / Revised: 18 November 2020 / Accepted: 21 November 2020 / Published: 25 November 2020

(This article belongs to the Special Issue Wearable Sensors & Gait)

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Abstract

The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was to predict foot strike angle and classify foot strike pattern from Loadsol^TM wearable pressure insoles using three machine learning techniques (multiple linear regression―MR, conditional inference tree―TREE, and random forest―FRST). Model performance was assessed using three-dimensional kinematics as a ground-truth measure. The prediction-model accuracy was similar for the regression, inference tree, and random forest models (RMSE: MR = 5.16°, TREE = 4.85°, FRST = 3.65°; MAPE: MR = 0.32°, TREE = 0.45°, FRST = 0.33°), though the regression and random forest models boasted lower maximum precision (13.75° and 14.3°, respectively) than the inference tree (19.02°). The classification performance was above 90% for all models (MR = 90.4%, TREE = 93.9%, and FRST = 94.1%). There was an increased tendency to misclassify mid foot strike patterns in all models, which may be improved with the inclusion of more mid foot steps during model training. Ultimately, wearable pressure insoles in combination with simple machine learning techniques can be used to predict and classify a runner’s foot strike with sufficient accuracy. View Full-Text

Keywords: decision tree; human running; random forest; regression; wearable devices

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MDPI and ACS Style

Moore, S.R.; Kranzinger, C.; Fritz, J.; Stӧggl, T.; Krӧll, J.; Schwameder, H. Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques. Sensors 2020, 20, 6737. https://doi.org/10.3390/s20236737

AMA Style

Moore SR, Kranzinger C, Fritz J, Stӧggl T, Krӧll J, Schwameder H. Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques. Sensors. 2020; 20(23):6737. https://doi.org/10.3390/s20236737

Chicago/Turabian Style

Moore, Stephanie R., Christina Kranzinger, Julian Fritz, Thomas Stӧggl, Josef Krӧll, and Hermann Schwameder. 2020. "Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques" Sensors 20, no. 23: 6737. https://doi.org/10.3390/s20236737

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Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques

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