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Home / Equine Research Bank / Sensors (Basel) / A Method to Estimate Horse Speed per Stride from One IMU wit...
Sensors (Basel, Switzerland)2020; 20(2); 518; doi: 10.3390/s20020518

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

Abstract: 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.
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Publication Date: 2020-01-17 PubMed ID: 31963422PubMed Central: PMC7014525DOI: 10.3390/s20020518Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research is about a method developed to calculate horse speed per stride from a single Inertial Measurement Unit (IMU) through machine learning, without needing a GPS system. This allows the model’s use in various conditions – whether indoor or outdoor. The developed method performs better than existing ones in terms of error margin and accuracy.

Overview of the Research

  • The study primarily focuses on developing a model to estimate horse speed per stride using data from an inertial measurement unit (IMU).
  • This research taps into the advancement of numerical sensors in sports and the growing demand for accurately measuring motion parameters.
  • The IMU, increasingly employed in the sports and clinical domain, utilizes accelerometric and gyroscopic data in estimating these parameters.

Objective & Methodology

  • The goal is to create a model suitable for integration into a smart device. The model should estimate horse speed per stride without appealing to a global positioning system, and should work in both indoor and outdoor settings.
  • The study pits two speed calculation methods against each other – a signal-based method and a machine-learning model. Both utilize accelerometric and gyroscopic data and do not require external input.
  • Data collection happened at various speeds, on both straight and curved routes.
  • Speed was measured using two reference systems, providing a benchmark against which the tested models’ accuracy was compared.

Evaluation & Findings

  • The evaluation of these models took place according to three criteria: error percentage above 0.6 m/s, Root Mean Square Error (RMSE), and the Bland and Altman agreement limit.
  • Results revealed the machine-learning model as the superior alternative, demonstrating the lowest values across all three evaluation criteria.
  • No similar research on horses existed for comparison, so the model was compared to those created for human walking.

Significance & Implications

  • The study’s salient contribution is the development of the first method to accurately calculate speed per stride for horses without necessitating a GPS or a magnetometer.
  • The model can also be extended to other equestrian sports, as well as bipedal locomotion, provided the machine learning model is taught with consistent data.
  • The accuracy of the machine learning model is attributed to the extensive database created to teach the model, and the novel way of dividing stride data before using them as model input.

Cite This Article

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

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 20
Issue: 2
PII: 518

Researcher Affiliations

Schmutz, Amandine
  • Lim France, Chemin Fontaine de Fanny, 24300 Nontron, France.
  • CWD-Vetlab, Ecole Nationale Vétérinaire d'Alfort, F-94700 Maisons-Alfort, France.
  • LBMC UMR T9406, Université de Lyon, Lyon 1, 69364 Lyon, France.
  • ERIC EA3083, Université de Lyon, Lyon 2, 69007 Lyon, France.
Chèze, Laurence
  • LBMC UMR T9406, Université de Lyon, Lyon 1, 69364 Lyon, France.
Jacques, Julien
  • ERIC EA3083, Université de Lyon, Lyon 2, 69007 Lyon, France.
Martin, Pauline
  • Lim France, Chemin Fontaine de Fanny, 24300 Nontron, France.
  • CWD-Vetlab, Ecole Nationale Vétérinaire d'Alfort, F-94700 Maisons-Alfort, France.

MeSH Terms

  • Accelerometry / methods
  • Animals
  • Equipment Design
  • Gait / physiology
  • Horses / physiology
  • Machine Learning
  • Signal Processing, Computer-Assisted

Grant Funding

  • contract ANR 16-LCV2-0002-01 / LabCom 'CWD-Vetlab'

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 6 times.
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