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Article

A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method

by 1,2,3,4,*,†, 3,†, 4,† and 1,2,†
1
Lim France, Chemin Fontaine de Fanny, 24300 Nontron, France
2
CWD-Vetlab, Ecole Nationale Vétérinaire d’Alfort, F-94700 Maisons-Alfort, France
3
LBMC UMR T9406, Université de Lyon, Lyon 1, 69364 Lyon, France
4
ERIC EA3083, Université de Lyon, Lyon 2, 69007 Lyon, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(2), 518; https://doi.org/10.3390/s20020518
Received: 13 November 2019 / Revised: 27 December 2019 / Accepted: 15 January 2020 / Published: 17 January 2020
(This article belongs to the Special Issue Human and Animal Motion Tracking Using Inertial Sensors)
With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or the sports one to estimate spatiotemporal parameters. The purpose of the present study was to develop a model that can be included in a smart device in order to estimate the horse speed per stride from accelerometric and gyroscopic data without the use of a global positioning system, enabling the use of such a tool in both indoor and outdoor conditions. The accuracy of two speed calculation methods was compared: one signal based and one machine learning model. Those two methods allowed the calculation of speed from accelerometric and gyroscopic data without any other external input. For this purpose, data were collected under various speeds on straight lines and curved paths. Two reference systems were used to measure the speed in order to have a reference speed value to compare each tested model and estimate their accuracy. Those models were compared according to three different criteria: the percentage of error above 0.6 m/s, the RMSE, and the Bland and Altman limit of agreement. The machine learning method outperformed its competitor by giving the lowest value for all three criteria. The main contribution of this work is that it is the first method that gives an accurate speed per stride for horses without being coupled with a global positioning system or a magnetometer. No similar study performed on horses exists to compare our work with, so the presented model is compared to existing models for human walking. Moreover, this tool can be extended to other equestrian sports, as well as bipedal locomotion as long as consistent data are provided to train the machine learning model. The machine learning model’s accurate results can be explained by the large database built to train the model and the innovative way of slicing stride data before using them as an input for the model. View Full-Text
Keywords: speed estimation; support vector machine; overall dynamic body acceleration; sensors; horse speed estimation; support vector machine; overall dynamic body acceleration; sensors; horse
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MDPI and ACS Style

Schmutz, A.; Chèze, L.; Jacques, J.; Martin, P. A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method. Sensors 2020, 20, 518. https://doi.org/10.3390/s20020518

AMA Style

Schmutz A, Chèze L, Jacques J, Martin P. A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method. Sensors. 2020; 20(2):518. https://doi.org/10.3390/s20020518

Chicago/Turabian Style

Schmutz, Amandine, Laurence Chèze, Julien Jacques, and Pauline Martin. 2020. "A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method" Sensors 20, no. 2: 518. https://doi.org/10.3390/s20020518

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