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Home / Equine Research Bank / PLoS One / Automatic hoof-on and -off detection in horses using hoof-mo...
PloS one2020; 15(6); e0233266; doi: 10.1371/journal.pone.0233266

Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors.

Abstract: For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions.
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Publication Date: 2020-06-03 PubMed ID: 32492034PubMed Central: PMC7269263DOI: 10.1371/journal.pone.0233266Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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 article discusses techniques for detecting hoof-on and hoof-off movements in horses using inertial measurement unit sensors (IMUs), which measure acceleration and angular velocity. Subsequent analysis compares the accuracy of different detection algorithms, showing great potential for use in gait classification.

Objective of the Study

  • The main objective of the study was to develop and test two algorithms capable of accurately detecting hoof-events (the moments when a horse’s hoof makes contact with the ground, and when it lifts off again) based on data gathered from hoof-mounted IMUs.

Experimental Method

  • The researchers conducted the experiment on seven Warmblood horses, which were equipped with two wireless IMUs. These sensors were attached to the right front and hind hooves.
  • The horses were then walked and trotted over a force plate, which provides another measure of hoof contact with the ground, allowing for internal validation of the IMU data.
  • The resultant acceleration and angular velocity signals produced by the IMUs were processed by the two developed algorithms for the automatic detection of hoof-on and hoof-off events.

Data Analysis and Results

  • The researchers used Bland Altman analysis and linear mixed model analysis to evaluate the agreement between the force plate readings and the hoof-event detections made by the two algorithms.
  • These analyses were performed separately for each algorithm and for both hoof-on and hoof-off events.
  • The results showed that for hoof-on detection, the angular velocity algorithm proved to be more accurate with varying degrees of precision depending on the horse’s gait and individual hoof being analyzed.
  • On the other hand, for hoof-off detection, the acceleration algorithm displayed the best accuracy and precision, with these metrics not affected by the horse’s gait or hoof.

Conclusion of the Study

  • The study concluded that the two algorithms developed have high potential utility in the area of gait classification.
  • However, the researchers suggest that further investigation is required to identify how different circumstances, such as surfaces and hoof trimming conditions, might affect the accuracy and applicability of these algorithms.

Cite This Article

APA
Tijssen M, Hernlund E, Rhodin M, Bosch S, Voskamp JP, Nielen M, Serra Braganςa FM. (2020). Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors. PLoS One, 15(6), e0233266. https://doi.org/10.1371/journal.pone.0233266

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 15
Issue: 6
Pages: e0233266
PII: e0233266

Researcher Affiliations

Tijssen, M
  • Department Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Hernlund, E
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Rhodin, M
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Bosch, S
  • Inertia Technology B.V., Enschede, The Netherlands.
  • Department of Computer Science, Pervasive Systems Group, University of Twente, Enschede, The Netherlands.
Voskamp, J P
  • Rosmark Consultancy, Wekerom, The Netherlands.
  • Department Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Nielen, M
  • Department Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Serra Braganςa, F M
  • Department Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.

MeSH Terms

  • Acceleration
  • Algorithms
  • Animals
  • Biomechanical Phenomena
  • Female
  • Forelimb / physiology
  • Gait / physiology
  • Gait Analysis / instrumentation
  • Gait Analysis / statistics & numerical data
  • Gait Analysis / veterinary
  • Hindlimb / physiology
  • Hoof and Claw / physiology
  • Horses / physiology
  • Linear Models
  • Male
  • Remote Sensing Technology / instrumentation
  • Remote Sensing Technology / statistics & numerical data
  • Remote Sensing Technology / veterinary
  • Running / physiology
  • Walking / physiology
  • Wireless Technology / instrumentation
  • Wireless Technology / statistics & numerical data

Conflict of Interest Statement

The authors have read the journal's policy and the authors of this manuscript have the following competing interests: S. Bosch is a paid employee of Inertia-Technology B.V., the company that sells the inertial sensor system used for data collection, and has received salary support for his role in this study. J.P. Voskamp is founder of Rosmark Consultancy and has received salary support for his role in this study. Inertia-Technology B.V., Rosmark Consultancy and Utrecht University are partners in the EquiMoves® corporation. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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Citations

This article has been cited 10 times.
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