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Home / Equine Research Bank / Sensors (Basel) / Using Different Combinations of Body-Mounted IMU Sensors to ...
Sensors (Basel, Switzerland)2021; 21(3); doi: 10.3390/s21030798

Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach.

Abstract: Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.
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Publication Date: 2021-01-26 PubMed ID: 33530288PubMed Central: PMC7865839DOI: 10.3390/s21030798Google Scholar: Lookup
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  • Journal Article

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 demonstrates how speed estimation in horses can be improved with machine learning models that utilize data from seven variously placed body-mounted Inertial Measurement Unit (IMU) sensors.

Objective of the Research

  • The study aims to propose a better method for estimating a horse’s speed by developing Machine Learning (ML) models that can adapt to varying signals from multiple body-mounted IMUs.

Methodology

  • Data from 40 Icelandic and Franches-Montagnes horses were gathered over different gaits consisting of walk, trot, tölt, pace, and canter.
  • A total of seven IMUs were attached to different parts of the horse’s body – sacrum, withers, head, and limbs to capture a wide range of motion patterns.
  • These motion patterns were input into different ML algorithms to form models capable of estimating the horse’s speed.

Results

  • The models were evaluated based on their accuracy in estimating speed for each gait, as well as their dependency on the position of the IMUs on the horse’s body.
  • The most accurate model managed to estimate speed with an error rate of just 0.25 m/s, making it more accurate than most current methods used in both equine and human speed estimation.

Conclusion

  • The study concluded that it is feasible to develop highly accurate horse speed estimation models using ML methodologies, functioning independently of the IMU(s) location on the body and gait type.
  • This opens new possibilities in locomotion research and biomechanical analysis, bypassing the limitations of GPS and standalone IMUs.

Cite This Article

APA
Darbandi H, Serra Bragança F, van der Zwaag BJ, Voskamp J, Gmel AI, Haraldsdóttir EH, Havinga P. (2021). Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach. Sensors (Basel), 21(3). https://doi.org/10.3390/s21030798

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 21
Issue: 3

Researcher Affiliations

Darbandi, Hamed
  • Pervasive Systems Group, Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands.
Serra Bragança, Filipe
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584 CM Utrecht, The Netherlands.
van der Zwaag, Berend Jan
  • Pervasive Systems Group, Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands.
  • Inertia Technology B.V., 7521 AG Enschede, The Netherlands.
Voskamp, John
  • Rosmark Consultancy, 6733 AA Wekerom, The Netherlands.
Gmel, Annik Imogen
  • Equine Department, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland.
  • Agroscope-Swiss National Stud Farm, Les Longs-Prés, 1580 Avenches, Switzerland.
Haraldsdóttir, Eyrún Halla
  • Equine Department, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland.
Havinga, Paul
  • Pervasive Systems Group, Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands.

MeSH Terms

  • Animals
  • Biomechanical Phenomena
  • Gait
  • Horses
  • Machine Learning
  • Torso
  • Walking

Grant Funding

  • Paardensprong / EFRO OP-Oost
  • 627001325 / Swiss federal Office for Agriculture

Conflict of Interest Statement

The authors declare no conflict of interest.

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