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Home / Equine Research Bank / Equine Vet J / How low can we go? Influence of sample rate on equine pelvic...
Equine veterinary journal2020; 53(5); 1075-1081; doi: 10.1111/evj.13371

How low can we go? Influence of sample rate on equine pelvic displacement calculated from inertial sensor data.

Abstract: Low-cost sensor devices are often limited in terms of sample rate. Based on signal periodicity, the Nyquist theorem allows determining the minimum theoretical sample rate required to adequately capture cyclical events, such as pelvic movement in trotting horses. Objective: To quantify the magnitude of errors arising with reduced sample rates when capturing biological signals using the example of pelvic time-displacement series and derived minima and maxima used to quantify movement asymmetry in lame horses. Methods: Data comparison. Methods: Root mean square (RMS) errors between the 'reference' time-displacement series, captured with a validated inertial sensor at 100 Hz sample rate, and down-sampled time-series (8 Hz to 50 Hz) are calculated. Accuracy and precision are determined for maxima and minima derived from the time-displacement series. Results: Average RMS errors are <2 mm at 50 Hz sample rate, <4 mm at 40 Hz, <7 mm between 25 and 35 Hz, and increase to up to 20 mm at 20 Hz and below. Accuracy for maxima and minima is generally below 1mm. Precision is 1 mm at 50 Hz sample rate, 3 mm at 40Hz and ≥9 mm at 20 Hz and below. Conclusions: Only sample rate, no other sensor parameters were investigated. Conclusions: Sample rate related errors for inertial sensor derived time-displacement series of pelvic movement are <2mm at 50 Hz, a rate that many low-cost loggers, smartphones or wireless sensors can sustain hence rendering these devices valid options for quantifying parameters relevant for lameness examinations in horses.
© 2020 EVJ Ltd.
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Publication Date: 2020-11-23 PubMed ID: 33113248DOI: 10.1111/evj.13371Google 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.

This research study investigates how the sample rate of low-cost sensor devices impacts the accuracy and precision in recording equine pelvic displacement. The study specifically uses the example of detecting movement asymmetry in lame horses.

Objective of the Research

  • The primary objective of the study was to examine the errors that might arise when capturing biological signals at reduced sample rates using low-cost sensor devices. The researchers used the example of time-displacement series related to equine pelvic movement to make their assessments.

Research Methodology

  • The study relies on data comparison as its primary methodology. This process involves measuring the Root Mean Square (RMS) errors between a ‘reference’ time-displacement series and down-sampled time-series.
  • The ‘reference’ time-displacement series was captured with a validated inertial sensor at a sample rate of 100 Hz, while the latter ranged from 8 to 50 Hz.
  • The accuracy and precision of derived maxima and minima from the time-displacement series were also analyzed.

Results of the Research

  • The study found that average RMS errors remained less than 2 mm at a sample rate of 50 Hz, while they were less than 4 mm at 40 Hz. The RMS errors were less than 7 mm between the sample rates of 25 and 35 Hz and increased to 20 mm at 20 Hz and below.
  • The accuracy for maxima and minima values was generally below 1mm.
  • The study also notes that precision was 1 mm at 50 Hz, 3 mm at 40 Hz, and equal to or greater than 9 mm at 20 Hz and below.

Conclusions

  • The research concludes that the sample rate of the sensor is a determinant factor for the data accuracy and precision, rather than other sensor parameters. This conclusion is based solely upon the investigation of sample rate-related errors.
  • It was found that when using inertial sensors to conduct time-displacement series of equine pelvic movement, the errors related to the sample rate were less than 2mm at 50 Hz.
  • The finding implies that many low-cost sensor devices, including smartphones or wireless sensors that operate at this rate, could offer viable options to quantify parameters crucial for lameness examinations in horses.

Cite This Article

APA
Pfau T, Reilly P. (2020). How low can we go? Influence of sample rate on equine pelvic displacement calculated from inertial sensor data. Equine Vet J, 53(5), 1075-1081. https://doi.org/10.1111/evj.13371

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 53
Issue: 5
Pages: 1075-1081

Researcher Affiliations

Pfau, Thilo
  • Department of Clinical Science and Services, The Royal Veterinary College, North Mymms, Hatfield, Hertfordshire, UK.
Reilly, Patrick
  • Department of Clinical Studies New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.

MeSH Terms

  • Animals
  • Biomechanical Phenomena
  • Gait
  • Horse Diseases
  • Horses
  • Lameness, Animal
  • Movement
  • Pelvis
  • Smartphone

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Citations

This article has been cited 9 times.
  1. Davíðsson HB, Rees T, Ólafsdóttir MR, Einarsson H. Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors. Animals (Basel) 2023 Jan 3;13(1).
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  2. Pfau T, Bolt DM, Fiske-Jackson A, Gerdes C, Hoenecke K, Lynch L, Perrier M, Smith RKW. Linear Discriminant Analysis for Investigating Differences in Upper Body Movement Symmetry in Horses before/after Diagnostic Analgesia in Relation to Expert Judgement. Animals (Basel) 2022 Mar 17;12(6).
    doi: 10.3390/ani12060762pubmed: 35327159google scholar: lookup
  3. Clayton H, MacKechnie-Guire R, Byström A, Le Jeune S, Egenvall A. Guidelines for the Measurement of Rein Tension in Equestrian Sport. Animals (Basel) 2021 Sep 30;11(10).
    doi: 10.3390/ani11102875pubmed: 34679895google scholar: lookup
  4. MacKechnie-Guire R, Pfau T. Differential Rotational Movement of the Thoracolumbosacral Spine in High-Level Dressage Horses Ridden in a Straight Line, in Sitting Trot and Seated Canter Compared to In-Hand Trot. Animals (Basel) 2021 Mar 20;11(3).
    doi: 10.3390/ani11030888pubmed: 33804702google scholar: lookup
  5. Pfau T, Forbes B, Sepulveda-Caviedes F, Chan Z, Weller R. Exploring Monthly Variation of Gait Asymmetry During In-Hand Trot in Thoroughbred Racehorses in Race Training. Animals (Basel) 2025 Aug 20;15(16).
    doi: 10.3390/ani15162449pubmed: 40867777google scholar: lookup
  6. Schampheleer J, Eerdekens A, Joseph W, Martens L, Deruyck M. Detecting Equine Gaits Through Rider-Worn Accelerometers. Animals (Basel) 2025 Apr 8;15(8).
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  7. Fan B, Zhang L, Cai S, Du M, Liu T, Li Q, Shull P. Influence of Sampling Rate on Wearable IMU Orientation Estimation Accuracy for Human Movement Analysis. Sensors (Basel) 2025 Mar 22;25(7).
    doi: 10.3390/s25071976pubmed: 40218489google scholar: lookup
  8. Forbes B, Ho W, Parkes RSV, Sepulveda Caviedes MF, Pfau T, Martel DR. Associations between Racing Thoroughbred Movement Asymmetries and Racing and Training Direction. Animals (Basel) 2024 Apr 3;14(7).
    doi: 10.3390/ani14071086pubmed: 38612325google scholar: lookup
  9. Pfau T, Landsbergen K, Davis BL, Kenny O, Kernot N, Rochard N, Porte-Proust M, Sparks H, Takahashi Y, Toth K, Scott WM. Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses. Sensors (Basel) 2023 Oct 12;23(20).
    doi: 10.3390/s23208414pubmed: 37896509google scholar: lookup
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