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Related Concept Videos

Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

637
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
637
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

749
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method.

Amandine Schmutz1,2,3,4, Laurence Chèze3, Julien Jacques4

  • 1Lim France, Chemin Fontaine de Fanny, 24300 Nontron, France.

Sensors (Basel, Switzerland)
|January 23, 2020
PubMed
Summary

A new machine learning model accurately estimates horse speed per stride using only accelerometric and gyroscopic data. This innovation eliminates the need for GPS, enabling indoor and outdoor use for equestrian sports and beyond.

Keywords:
horseoverall dynamic body accelerationsensorsspeed estimationsupport vector machine

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Area of Science:

  • Biomechanics
  • Sports Technology
  • Machine Learning in Sports

Background:

  • Inertial measurement units (IMUs) are increasingly used in sports and clinical settings to estimate spatiotemporal parameters.
  • Accurate, objective motion parameter computation is crucial with the rise of numerical sensors in sports.

Purpose of the Study:

  • To develop a smart device-compatible model for estimating horse speed per stride using accelerometric and gyroscopic data.
  • To enable accurate speed estimation without Global Positioning System (GPS) or magnetometer reliance, facilitating indoor/outdoor application.

Main Methods:

  • Collected accelerometric and gyroscopic data across various speeds on straight and curved paths.
  • Compared a signal-based method with a machine learning model for speed calculation.
  • Evaluated model accuracy using percentage error above 0.6 m/s, Root Mean Square Error (RMSE), and Bland and Altman limits of agreement.

Main Results:

  • The machine learning model significantly outperformed the signal-based method across all accuracy criteria.
  • The study presents the first method for accurate, GPS-independent horse speed per stride estimation.
  • The developed model demonstrated superior performance compared to existing human locomotion models.

Conclusions:

  • Machine learning offers a highly accurate solution for estimating horse speed per stride from IMU data.
  • This technology has potential applications beyond equestrian sports, including bipedal locomotion analysis.
  • The model's success is attributed to a large training database and innovative data processing techniques.