FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
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.
Get Full Text
Publication Date: 2020-11-23 PubMed ID: 33113248DOI: 10.1111/evj.13371Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • 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.

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

References

This article includes 27 references
  1. Hoyt DF, Wickler SJ, Dutto DJ, Catterfeld GE, Johnsen D. What are the relations between mechanics, gait parameters, and energetics in terrestrial locomotion?. J Exp Zool 2006;305A:912-22.
  2. Audigié F, Pourcelot P, Degueurce C, Geiger D, Denoix JM. Fourier analysis of trunk displacements: a method to identify the lame limb in trotting horses. J Biomech 2002;35:1173-82.
  3. Manor B, Yu W, Zhu H, Harrison R, Lo O-Y, Lipsitz L. Smartphone App-Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability. JMIR Mhealth Uhealth 2018;6:e36.
  4. Ellis RJ, Ng YS, Zhu S, Tan DM, Anderson B, Schlaug G. A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease. PLoS One 2015;10:e0141694.
  5. Nishiguchi S, Yamada M, Nagai K, Mori S, Kajiwara Y, Sonoda T. Reliability and Validity of Gait Analysis by Android-Based Smartphone. Telemedicine and e-Health 2012;18:292-6.
  6. Furrer M, Bichsel L, Niederer M, Baur H, Schmid S. Validation of a smartphone-based measurement tool for the quantification of level walking. Gait Posture 2015;42:289-94.
  7. Steins D, Sheret I, Dawes H, Esser P, Collett J. A smart device inertial-sensing method for gait analysis. J Biomech 2014;47:3780-5.
  8. Mourcou Q, Fleury A, Franco C, Klopcic F, Vuillerme N. Performance Evaluation of Smartphone Inertial Sensors Measurement for Range of Motion. Sensors 2015;15:23168-87.
  9. Jarchi D, Pope J, Lee TKM, Tamjidi L, Mirzaei A, Sanei S. A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications. IEEE Rev Biomed Eng 2018;11:177-94.
  10. Pfau T, Weller R. Comparison of a standalone consumer grade smartphone to a specialist inertial measurement unit for quantification of movement symmetry in the trotting horse. Equine Vet J 2017;49:124-9.
  11. Warner SM, Koch TO, Pfau T. Inertial sensors for assessment of back movement in horses during locomotion over ground. Equine Vet J 2010;42(Suppl. 38):417-24.
  12. van Weeren PR, Pfau T, Rhodin M, Roepstorff L, Serra Bragança F, Weishaupt MA. Do we have to redefine lameness in the era of quantitative gait analysis?. Equine Vet J 2017;49:567-9.
  13. Bathe A, Judy CE, Dyson SJ. Letter to the Editor: Do we have to redefine lameness in the era of quantitative gait analysis ?. Equine Vet J 2018;50:273.
  14. Adair S, Baus M, Belknap J, Bell R, Boero M, Bussy C. Response to Letter to the Editor : Do we have to redefine lameness in the era of quantitative gait analysis. Equine Vet J 2018;50:415-7.
  15. van Weeren R. Letter to the Editor: On the origin of lameness - do opinions differ less than it might appear at first glance?. Equine Vet J 2019;51:557-8.
  16. McCracken MJ, Kramer J, Keegan KG, Lopes M, Wilson DA, Reed SK. Comparison of an inertial sensor system of lameness quantification with subjective lameness evaluation. Equine Vet J 2012;44:652-6.
  17. Pfau T, Witte TH, Wilson AM. A method for deriving displacement data during cyclical movement using an inertial sensor. The Journal of Experimental Biology 2005;208:2503-14.
  18. Serra Bragança FM, Roepstorff C, Rhodin M, Pfau T, van Weeren PR, Roepstorff L. Quantitative lameness assessment in the horse based on upper body movement symmetry: The effect of different filtering techniques on the quantification of motion symmetry. Biomed Signal Process Control 2020;57:101674.
  19. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10.
  20. Keegan KG, Kramer J, Yonezawa Y, Maki H, Pai PF, Dent EV. Assessment of repeatability of a wireless inertial sensor-based lameness evaluation system for horses. Am J Vet Res 2011;72:1156-63.
  21. Halling Thomsen M, Tolver Jensen A, Sørensen H, Lindegaard C, Haubro Andersen P. Symmetry indices based on accelerometric data in trotting horses. J Biomech 2010;43:2608-12.
  22. van Weeren PR, van den Bogert AJ, Barneveld A. A quantitative analysis of skin displacement in the trotting horse. Equine Vet J 1990;22(9):101-9.
  23. Pfau T, Caviedes MFS, Mccarthy R, Cheetham L, Forbes B, Rhodin M. Comparison of visual lameness scores to gait asymmetry in racing Thoroughbreds during trot in-hand. Equine Vet Educ 2018;32:191-8.
  24. Sepulveda Caviedes MF, Forbes BS, Pfau T. Repeatability of gait analysis measurements in Thoroughbreds in training. Equine Vet J 2018;50:513-8.
  25. Brenton RS, Thompson HS, Maxner C. Critical Flicker Frequency: A New Look at an Old Test. New Methods of Sensory Visual Testing Springer, New York, NY; 1989. pp 29-52.
    doi: 10.1007/978-1-4613-8835-7_3google scholar: lookup
  26. Hecht S, Shlaer S. Intermittent stimulation by light: the relation between intensity and critical frequency for different parts of the spectrum. J Gen Physiol 1936;19:965-77.
  27. Parkes RSV, Weller R, Groth AM, May S, Pfau T. Evidence of the development of ‘domain-restricted’ expertise in the recognition of asymmetric motion characteristics of hindlimb lameness in the horse. Equine Vet J 2009;41:112-7.

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).
    doi: 10.3390/ani13010183pubmed: 36611791google scholar: lookup
  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).
    doi: 10.3390/ani15081080pubmed: 40281916google scholar: lookup
  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
Subscribe to our newsletter
Join 99,658 horse owners like you who receive our email updates on horse health and nutrition.