FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Transl Anim Sci / Sensor analysis and initial assessment of detectable first h...
Translational animal science2019; 3(4); 1389-1398; doi: 10.1093/tas/txz089

Sensor analysis and initial assessment of detectable first hoof contacts and last break-overs as unique signal fluctuations for equine gait analysis.

Abstract: The objective of the control study was to assess 2 prominent fluctuations in a single optical signal as being either a true first hoof contact or a last break-over based on descriptive measures. The study builds on initial findings from a preliminary investigation of the embedded-optical-base system's (EOBS) capabilities in signal capturing and feasibility as potential alternative to existing gait technologies, such as piezoelectric (e.g., load cell) systems. Hoof contacts and break-overs were measured (0 to 1 au; arbitrary units) using a 2.4-m (length) × 0.9-m (width) platform containing 1 EOBS. Three mixed-breed horses ( = 3) were injected with saline or either 100 IU or 200 IU Botox (i.e., onabotulinumtoxinA) with a 2.5-mL final volume. Injections were made into the deep digital flexor muscle at the motor end plates, with electromyography and ultrasound guidance. Horses were observed for 3 time points (pre-, post-, and recovery test days) over the span of a 4-mo period. Signal fluctuations [i.e., amplitude of hoof impacts based on true first hoof contacts (Δ ) and true last break-overs (Δ )] and kinematics [i.e., complete gait pass (CGP) time duration ()] were recorded from each horse. Visual observations and video analysis were used for determining gait pattern categories. Individual horse measurements were analyzed for each trial, compared with video data and classified. Comparison of primary signal fluctuations (i.e., Δ vs. Δ ; forelimb vs. hindlimb) exhibited significant differences between hoof contacts and break-overs ( < 0.05). Right and left forelimb hoof contacts and hindlimb break-overs were not significantly different ( = 0.966; 0.063 ± 0.135; Estimate ± SE; = 0.606; 0.176 ± 0.142; Estimate ± SE, respectively). Additionally, treatment vs. saline forelimbs did not exhibit significant difference ( = 0.7407; -0.098 ± 0.279; Estimate ± SE). Overall, data showed that the EOBS can collect repeatable and unique primary signal fluctuations as prominent and different gait measurements providing evidence to further development and research of the sensing system.
© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science.
Get Full Text
Publication Date: 2019-06-04 PubMed ID: 32704902PubMed Central: PMC7200566DOI: 10.1093/tas/txz089Google 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 article focuses on investigating two unique signal fluctuations for equine gait analysis by using sensor-based technologies. The aim was to differentiate between the horse’s first hoof contact and the last break-over using an embedded optical-based system (EOBS). The underlying idea was to find better and repeatable alternative methods for equine gait analysis and measurement.

Research Methodology and Execution

  • The study involved three mixed breed horses which were subjected to two variations of the condition: a saline injection or Botox injection at varying concentrations (100 IU or 200 IU) into the deep digital flexor muscle.
  • The injections were guided by electromyography and ultrasound for efficiency.
  • The horses were observed over three different time points: pre-injection, post-injection, and during recovery, all spread over a four-month period.
  • A specially-crafted platform equipped with an EOBS was used to capture the first hoof contacts and the last break-overs of the horses. The signals were measured using arbitrary units (au).

Data Collection and Analysis

  • The data collected included the amplitude of hoof impacts based on first hoof contacts (Δ) and last break-overs (Δ) along with the complete gait pass (CGP) time duration from every horse.
  • Visual observations and video analysis were simultaneously employed for categorizing the gait patterns in each instance.
  • The data gathered from the EOBS was then compared against the video data to assess the accuracy and reliability of the system.

Research Findings

  • The study revealed significant differences between the first hoof contacts and last break-overs when comparing the primary signal fluctuations (forelimb vs. hindlimb). However, the results between right and left forelimb hoof contacts and hindlimb break-overs did not vary significantly.
  • No significant difference was found between the saline injections and treatment forelimbs.
  • The optical signal fluctuations captured by the EOBS were unique and repeatable, suggesting the system could serve as a new method for gait measurement and analysis.

Conclusion and Further Research

  • The study concluded that the EOBS can consistently capture unique signal fluctuations as different and prominent gait measurements, hence, providing substantial evidence to justify further research and development of the sensing system.

Cite This Article

APA
Atkins CA, Pond KR, Madsen CK, Moorman VJ, Roman-Muniz IN, Archibeque SL, Grandin T. (2019). Sensor analysis and initial assessment of detectable first hoof contacts and last break-overs as unique signal fluctuations for equine gait analysis. Transl Anim Sci, 3(4), 1389-1398. https://doi.org/10.1093/tas/txz089

Publication

Translational animal science
ISSN: 2573-2102
NlmUniqueID: 101738705
Country: England
Language: English
Volume: 3
Issue: 4
Pages: 1389-1398

Researcher Affiliations

Atkins, Colton A
  • Department of Animal Sciences, College of Agriculture Sciences, Colorado State University, Fort Collins, CO.
Pond, Kevin R
  • Paul Engler College of Agriculture and Natural Sciences, West Texas A&M, Canyon, TX.
Madsen, Christi K
  • Department of Electrical and Computer Engineering, College of Engineering, Texas A&M University, College Station, TX.
Moorman, Valerie J
  • Equine Orthopaedic Research Center, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Fort Collins, CO.
Roman-Muniz, Ivette N
  • Department of Animal Sciences, College of Agriculture Sciences, Colorado State University, Fort Collins, CO.
Archibeque, Shawn L
  • Department of Animal Sciences, College of Agriculture Sciences, Colorado State University, Fort Collins, CO.
Grandin, Temple
  • Department of Animal Sciences, College of Agriculture Sciences, Colorado State University, Fort Collins, CO.

References

This article includes 34 references
  1. Barton K. MuMIn: multi-model inference. R package version 1.42.1.
  2. Bates D, Maechler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67:1–48.
    doi: 10.18637/jss.v067.i01google scholar: lookup
  3. Baxter G M, Stashak T S, Hill C. Conformation and movement. In: Baxter G. M., editor, Adams and Stashak’s lameness in horses. 6th ed Wiley-Blackwell, Philadelphia, PA: p. 73.
  4. Buchner HH, Savelberg HH, Schamhardt HC, Barneveld A. Limb movement adaptations in horses with experimentally induced fore- or hindlimb lameness.. Equine Vet J 1996 Jan;28(1):63-70.
    pubmed: 8565956doi: 10.1111/j.2042-3306.1996.tb01591.xgoogle scholar: lookup
  5. Carter D W, Renfroe J B. A novel approach to the treatment and prevention of laminitis: botulinum toxin type A for the treatment of laminitis. J. Equine Vet. Sci. 29:595–600.
    doi: 10.1016/j.jevs.2009.05.008.google scholar: lookup
  6. Chapinal N, de Passillé AM, Rushen J, Wagner S. Automated methods for detecting lameness and measuring analgesia in dairy cattle.. J Dairy Sci 2010 May;93(5):2007-13.
    doi: 10.3168/jds.2009-2803.pubmed: 20412914google scholar: lookup
  7. Clarkson MJ, Downham DY, Faull WB, Hughes JW, Manson FJ, Merritt JB, Murray RD, Russell WB, Sutherst JE, Ward WR. Incidence and prevalence of lameness in dairy cattle.. Vet Rec 1996 Jun 8;138(23):563-7.
    doi: 10.1136/vr.138.23.563.pubmed: 8795183google scholar: lookup
  8. Conte S, Bergeron R, Gonyou H, Brown J, Rioja-Lang FC, Connor L, Devillers N. Measure and characterization of lameness in gestating sows using force plate, kinematic, and accelerometer methods.. J Anim Sci 2014 Dec;92(12):5693-703.
    doi: 10.2527/jas.2014-7865.pubmed: 25403203google scholar: lookup
  9. Flower FC, Sanderson DJ, Weary DM. Hoof pathologies influence kinematic measures of dairy cow gait.. J Dairy Sci 2005 Sep;88(9):3166-73.
    doi: 10.3168/jds.S0022-0302(05)73000-9.pubmed: 16107407google scholar: lookup
  10. Goodwin L D, Leech N L. Understanding correlation: factors that affect the size of r. J. Exp. Edu. 74:251–266.
  11. Gross J, Ligges U. nortest: Tests for normality. R package version 1.0-4. 294.
  12. Haley DB, de Passillé AM, Rushen J. Assessing cow comfort: effects of two floor types and two tie stall designs on the behaviour of lactating dairy cows.. Appl Anim Behav Sci 2001 Feb 20;71(2):105-117.
    pubmed: 11179563doi: 10.1016/s0168-1591(00)00175-1google scholar: lookup
  13. Hardeman LC, van der Meij BR, Oosterlinck M, Veraa S, van der Kolk JH, Wijnberg ID, Back W. Effect of Clostridium botulinum toxin type A injections into the deep digital flexor muscle on the range of motion of the metacarpus and carpus, and the force distribution underneath the hooves, of sound horses at the walk.. Vet J 2013 Dec;198 Suppl 1:e152-6.
    doi: 10.1016/j.tvjl.2013.09.051.pubmed: 24360731google scholar: lookup
  14. Herlin AH, Drevemo S. Investigating locomotion of dairy cows by use of high speed cinematography.. Equine Vet J Suppl 1997 May;(23):106-9.
    pubmed: 9354302doi: 10.1111/j.2042-3306.1997.tb05066.xgoogle scholar: lookup
  15. Keegan KG, Pai PF, Wilson DA, Smith BK. Signal decomposition method of evaluating head movement to measure induced forelimb lameness in horses trotting on a treadmill.. Equine Vet J 2001 Sep;33(5):446-51.
    doi: 10.2746/042516401776254781.pubmed: 11558738google scholar: lookup
  16. Krohn C C, Munksgaard L. Behaviour of dairy cows kept in extensive (loose housing/pasture) or intensive (tie stall) environments. II. Lying and lying-down behaviour. Appl. Anim. Behav. Sci. 37:1–16.
    doi: 10.1016/0168-1591(93)90066-X.google scholar: lookup
  17. Lenth R V. Least-squares means: the R package lsmeans. J. Stat. Softw. 69:1–33.
    doi: 10.18637/jss.v069.i01google scholar: lookup
  18. Maertens W, Vangeyte J, Baert J, Jantuan A, De Campeneere S, Pluk A, Opsomer G, Van Weyenberg S, Van Nuffel A. Development of a real time cow gait tracking and analyzing tool to assess lameness using a pressure sensitive walkway: the GAITWISE system. Biosyst. Eng. 110:29–39.
    doi: 10.1016/j.biosystemseng.2011.06.003.google scholar: lookup
  19. Meyer SW, Weishaupt MA, Nuss KA. Gait pattern of heifers before and after claw trimming: a high-speed cinematographic study on a treadmill.. J Dairy Sci 2007 Feb;90(2):670-6.
    doi: 10.3168/jds.S0022-0302(07)71549-7.pubmed: 17235142google scholar: lookup
  20. Moorman VJ, Reiser RF 2nd, Mahaffey CA, Peterson ML, McIlwraith CW, Kawcak CE. Use of an inertial measurement unit to assess the effect of forelimb lameness on three-dimensional hoof orientation in horses at a walk and trot.. Am J Vet Res 2014 Sep;75(9):800-8.
    doi: 10.2460/ajvr.75.9.800.pubmed: 25157883google scholar: lookup
  21. Moorman VJ, Reiser RF 2nd, Peterson ML, McIlwraith CW, Kawcak CE. Effect of forelimb lameness on hoof kinematics of horses at a trot.. Am J Vet Res 2013 Sep;74(9):1183-91.
    doi: 10.2460/ajvr.74.9.1183.pubmed: 23977890google scholar: lookup
  22. Moorman VJ, Reiser RF 2nd, Peterson ML, McIlwraith CW, Kawcak CE. Effect of forelimb lameness on hoof kinematics of horses at a walk.. Am J Vet Res 2013 Sep;74(9):1192-7.
    doi: 10.2460/ajvr.74.9.1192.pubmed: 23977891google scholar: lookup
  23. Pairis-Garcia MD, Johnson AK, Abell CA, Coetzee JF, Karriker LA, Millman ST, Stalder KJ. Measuring the efficacy of flunixin meglumine and meloxicam for lame sows using a GAITFour pressure mat and an embedded microcomputer-based force plate system.. J Anim Sci 2015 May;93(5):2100-10.
    doi: 10.2527/jas.2014-8796.pubmed: 26020306google scholar: lookup
  24. Pastell M, Takko H, Grohn H, Hautala M, Poikalainen V, Praks J, Veermae I, Kujala M, Ahokas J. Assessing cow’s welfare: weighing the cow in a milking robot. Biosyst. Eng. 93:81–87.
    doi: 10.1016/j.biosystemseng.2005.09.009.google scholar: lookup
  25. Pluk A, Bahr C, Poursaberi A, Maertens W, van Nuffel A, Berckmans D. Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques.. J Dairy Sci 2012 Apr;95(4):1738-48.
    doi: 10.3168/jds.2011-4547.pubmed: 22459822google scholar: lookup
  26. Prankel S, Corbett C, Bevins J, Davies J. Biomechanical analysis in veterinary practice. In Practice 38:176–187.
    doi: 10.1136/inp.i1458.google scholar: lookup
  27. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria: Version 3.5.0 - “Joy in Playing”.
  28. RStudio Team. RStudio: integrated development for R. RStudio, Inc, Boston, MA, Version 1.1.453.
  29. Scott GB. Changes in limb loading with lameness for a number of Friesian cattle.. Br Vet J 1989 Jan-Feb;145(1):28-38.
    doi: 10.1016/0007-1935(89)90005-5.pubmed: 2920273google scholar: lookup
  30. Tasch U, Rajkondawar P. The development of a SoftSeparatorTM for a lameness diagnostic system. Comput. Electron. Agric. 44:239–245.
    doi: 10.1016/j.compag.2004.04.001.google scholar: lookup
  31. Tsang R, Colley L, Lynd LD. Inadequate statistical power to detect clinically significant differences in adverse event rates in randomized controlled trials.. J Clin Epidemiol 2009 Jun;62(6):609-16.
    doi: 10.1016/j.jclinepi.2008.08.005.pubmed: 19013761google scholar: lookup
  32. USDA. National economic cost of equine lameness, colic, and equine protozoal myeloencephalitis in the United States. .
  33. Wijnberg ID, Hardeman LC, van der Meij BR, Veraa S, Back W, van der Kolk JH. The effect of Clostridium botulinum toxin type A injections on motor unit activity of the deep digital flexor muscle in healthy sound Royal Dutch sport horses.. Vet J 2013 Dec;198 Suppl 1:e147-51.
    doi: 10.1016/j.tvjl.2013.09.050pubmed: 24360760google scholar: lookup
  34. Zhao K, Bewley J M, He D, Jin X. Automatic lameness detection in dairy cattle based on leg swing analysis with an image processing technique. Comput. Electron. Agric. 148:226–236.
    doi: 10.1016/j.compag.2018.03.014google scholar: lookup

Citations

This article has been cited 1 times.
  1. Crecan CM, Morar IA, Lupsan AF, Repciuc CC, Rus MA, Pestean CP. Development of a Novel Approach for Detection of Equine Lameness Based on Inertial Sensors: A Preliminary Study.. Sensors (Basel) 2022 Sep 19;22(18).
    doi: 10.3390/s22187082pubmed: 36146429google scholar: lookup
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.