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Sensors (Basel, Switzerland)2020; 20(10); doi: 10.3390/s20102983

Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb.

Abstract: The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.
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Publication Date: 2020-05-25 PubMed ID: 32466104PubMed Central: PMC7288211DOI: 10.3390/s20102983Google 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.

This study discusses the development and evaluation of four methods to detect horse trotting stances using an inertial measurement unit (IMU) mounted on the horse’s limb. It suggests that methods using the gyroscope within the IMU present potential for more precise detection of stances, despite individual differences between horses.

Objective and Key Concepts

The focus of this research is to compare four methods of detecting horse trotting stances using an IMU, a sensor device, mounted on the horse’s canon bone. IMUs are sensors which measure body motion, and in this case, are used to examine the locomotion or movement pattern of horses. The implication of this research is towards improving our understanding of horse locomotion, particularly by detecting abnormalities such as lameness.

  • Inertial Measurement Unit (IMU): This sensor technology captures motion data. IMUs contain accelerometers and gyroscopes, both of which contribute to detecting body motion.
  • Trotting Stance: A specific phase in horse gait that represents the diagonal stance phase when a horse is trotting.
  • Accelerometric and Gyroscopic Data: Accelerometric data relates to the acceleration forces, whereas gyroscopic data refers to the angular velocity or rotational movement.

Lameness Detection Methods

The study examines four methods for stride detection. These methods use either thresholding or wavelets to detect stride events, and rely on accelerometer and gyroscope data.

  • Thresholding: This method uses a set value, or “threshold,” to signal when a stride event occurs. When the data from the sensor crosses this threshold, it indicates a stride or gait phase.
  • Wavelets: This method converts the raw sensor data into a series of “wavelets,” or small waves, making it easier to detect patterns in the data indicative of stride events.

Research Findings

The research found that methods developed from gyroscopic data demonstrated more precision than those that utilized accelerometric data. The gyroscope was less impacted by differences in stride patterns between individual horses.

  • Precision of Gyroscope: The methods utilizing gyroscopic data showed less than 0.6% bias (or deviation from the truth) when detecting and 0.1% when detecting , indicating a high level of accuracy.
  • Adaptability: The gyroscope was less sensitive to variations in the stride pattern of individual horses, suggesting that the gyroscope-based methods were more adaptive to individual differences among horses.

Implications of the Study

The findings suggest that utilizing gyroscope data from an IMU mounted on the horse’s limb could enhance the study of horse locomotion. This includes potential applications in analyzing different gait paces and even the influence of different ground types on a horse’s gait. These developments could provide new insights into equine health and performance, and could be of particular relevance in detecting lameness.

Cite This Article

APA
Sapone M, Martin P, Ben Mansour K, Château H, Marin F. (2020). Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb. Sensors (Basel), 20(10). https://doi.org/10.3390/s20102983

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 20
Issue: 10

Researcher Affiliations

Sapone, Marie
  • Université de Technologie de Compiègne, Alliance Sorbonne Université, UMR CNRS 7338 BioMécanique et BioIngénierie, 60200 Compiègne, France.
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CWD-VetLab, 94700 Maisons-Alfort, France.
  • LIM France, Chemin Fontaine de Fanny, 24300 Nontron, France.
Martin, Pauline
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CWD-VetLab, 94700 Maisons-Alfort, France.
  • LIM France, Chemin Fontaine de Fanny, 24300 Nontron, France.
Ben Mansour, Khalil
  • Université de Technologie de Compiègne, Alliance Sorbonne Université, UMR CNRS 7338 BioMécanique et BioIngénierie, 60200 Compiègne, France.
Château, Henry
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CWD-VetLab, 94700 Maisons-Alfort, France.
Marin, Frédéric
  • Université de Technologie de Compiègne, Alliance Sorbonne Université, UMR CNRS 7338 BioMécanique et BioIngénierie, 60200 Compiègne, France.

MeSH Terms

  • Accelerometry
  • Animals
  • Biomechanical Phenomena
  • Female
  • Foot
  • Gait
  • Horses
  • Locomotion
  • Male
  • Movement Disorders

Grant Funding

  • ANR 16-LCV2-0002-01 / Agence Nationale de la Recherche
  • CIFRE n°2017/0267 / Association Nationale de la Recherche et de la Technologie
  • contract for support of innovative projects n°18002380 / Région Nouvelle Aquitaine

Conflict of Interest Statement

The project was supported by the Agence National de Recherche Technologique (ANRT) (CIFRE n°2017/0267) which sponsors collaborative project between company (LIM France), and universities (the Université de technologie de Compiègne (UTC) and the Ecole Nationale Vétérinaire d’Alfort (ENVA)).

References

This article includes 54 references
  1. Barrey E. Methods, Applications and Limitations of Gait Analysis in Horses. Vet. J. 1999;157:7–22.
    doi: 10.1053/tvjl.1998.0297pubmed: 10030124google scholar: lookup
  2. Wilson AM, Watson JC, Lichtwark GA. Biomechanics: A catapult action for rapid limb protraction. Nature 2003;421:35–36.
    doi: 10.1038/421035apubmed: 12511944google scholar: lookup
  3. Biewener AA, Thomason J, Goodship A, Lanyon LE. Bone stress in the horse forelimb during locomotion at different gaits: A comparison of two experimental methods. J. Biomech. 1983;16:565–576.
    doi: 10.1016/0021-9290(83)90107-0pubmed: 6643529google scholar: lookup
  4. Crevier-Denoix N, Falala S, Holden-Douilly L, Camus M, Martino J, Ravary-Plumioen B, Vergari C, Desquilbet L, Denoix JM, Chateau H. Comparative kinematic analysis of the leading and trailing forelimbs of horses cantering on a turf and a synthetic surface: Forelimb kinematics at the canter on turf and synthetic surfaces. Equine Vet. J. 2013;45:54–61.
    doi: 10.1111/evj.12160pubmed: 24304405google scholar: lookup
  5. Murray RC, Dyson SJ, Tranquille C, Adams V. Association of type of sport and performance level with anatomical site of orthopaedic injury diagnosis. Equine Vet. J. 2006;38:411–416.
    doi: 10.1111/j.2042-3306.2006.tb05578.xpubmed: 17402457google scholar: lookup
  6. Meershoek LS, Roepstorff L, Schamhardt HC, Johnston C, Bobbert MF. Joint moments in the distal forelimbs of jumping horses during landing. Equine Vet. J. 2010;33:410–415.
    doi: 10.2746/042516401776249570pubmed: 11469776google scholar: lookup
  7. Singer ER, Barnes J, Saxby F, Murray JK. Injuries in the event horse: Training versus competition. Vet. J. 2008;175:76–81.
    doi: 10.1016/j.tvjl.2006.11.009pubmed: 17204438google scholar: lookup
  8. Buchner HHF, Savelberg HHCM, Schamhardt HC, Barneveld A. Head and trunk movement adaptations in horses with experimentally induced fore-or hindlimb lameness. Equine Vet. J. 1996;28:71–76.
    doi: 10.1111/j.2042-3306.1996.tb01592.xpubmed: 8565958google scholar: lookup
  9. Kramer J, Keegan KG, Wilson DA, Smith BK, Wilson DJ. Kinematics of the hind limb in trotting horses after induced lameness of the distal intertarsal and tarsometatarsal joints and intra-articular administration of anesthetic. Am. J. Vet. Res. 2000;61:1031–1036.
    doi: 10.2460/ajvr.2000.61.1031pubmed: 10976732google scholar: lookup
  10. 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;33:446–451.
    doi: 10.2746/042516401776254781pubmed: 11558738google scholar: lookup
  11. Nguyen MD, Mun KR, Jung D, Han J, Park M, Kim J, Kim J. IMU-based Spectrogram Approach with Deep Convolutional Neural Networks for Gait Classification. Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE); Las Vegas, NV, USA. 4–6 January 2020; pp. 1–6.
  12. Sahoo S, Saboo M, Pratihar DK, Mukhopadhyay S. Real-Time Detection of Actual and Early Gait Events During Level-Ground and Ramp Walking. IEEE Sens. J. 2020.
    doi: 10.1109/JSEN.2020.2980863google scholar: lookup
  13. Tumkur K, Subbiah S. Modeling Human Walking for Step Detection and Stride Determination by 3-Axis Accelerometer Readings in Pedometer. Proceedings of the 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation; Kuantan, Malaysia. 25–27 September 2012; pp. 199–204.
  14. Anwary AR, Yu H, Vassallo M. Optimal Foot Location for Placing Wearable IMU Sensors and Automatic Feature Extraction for Gait Analysis. IEEE Sens. J. 2018;18:2555–2567.
    doi: 10.1109/JSEN.2017.2786587google scholar: lookup
  15. Salarian A, Russmann H, Vingerhoets FJG, Dehollain C, Blanc Y, Burkhard PR, Aminian K. Gait Assessment in Parkinson’s Disease: Toward an Ambulatory System for Long-Term Monitoring. IEEE Trans. Biomed. Eng. 2004;51:1434–1443.
    doi: 10.1109/TBME.2004.827933pubmed: 15311830google scholar: lookup
  16. Doheny EP, Foran TG, Greene BR. A single gyroscope method for spatial gait analysis. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology; Buenos Aires, Argentina. 31 August–4 September 2010; pp. 1300–1303.
    pubmed: 21095923
  17. Caramia C, De Marchis C, Schmid M. Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds. Sensors 2019;19:1869.
    doi: 10.3390/s19081869pmc: PMC6515071pubmed: 31010114google scholar: lookup
  18. Sapone M, Martin P, Chateau H, Parmentier J, Ben Mansour K, Marin F. Sizing of inertial sensors adapted to measurement of locomotor parameters in horses using motion capture. Comput. Methods Biomech. Biomed. Eng. 2019;22:S1–S393.
  19. Drevemo S, Dalin G, Fredricson I, Hjerten G. Equine locomotion: 1. The analysis of linear and temporal stride characteristics of trotting Standardbreds. Equine Vet. J. 1980;12:60–65.
    doi: 10.1111/j.2042-3306.1980.tb02310.xpubmed: 7371611google scholar: lookup
  20. Olsen E, Haubro Andersen P, Pfau T. Accuracy and Precision of Equine Gait Event Detection during Walking with Limb and Trunk Mounted Inertial Sensors. Sensors 2012;12:8145–8156.
    doi: 10.3390/s120608145pmc: PMC3436021pubmed: 22969392google scholar: lookup
  21. Hodson E, Clayton HM, Lanovaz JL. The forelimb in walking horses: 1. Kinematics and ground reaction forces. Equine Vet. J. 2000;32:287–294.
    doi: 10.2746/042516400777032237pubmed: 10952376google scholar: lookup
  22. Merkens HW, Schamhardt HC. Relationships between ground reaction force patterns and kinematics in the walking and trotting horse. Equine Vet. J. 1994;26:67–70.
    doi: 10.1111/j.2042-3306.1994.tb04877.xpubmed: 0google scholar: lookup
  23. Witte TH. Determination of peak vertical ground reaction force from duty factor in the horse (Equus caballus). J. Exp. Biol. 2004;207:3639–3648.
    doi: 10.1242/jeb.01182pubmed: 15371472google scholar: lookup
  24. Boye JK, Thomsen MH, Pfau T, Olsen E. Accuracy and precision of gait events derived from motion capture in horses during walk and trot. J. Biomech. 2014;47:1220–1224.
    doi: 10.1016/j.jbiomech.2013.12.018pubmed: 24529754google scholar: lookup
  25. Galisteo AM, Garrido-Castro JL, Miró F, Plaza C, Medina-Carnicer R. Assessment of a method to determine the stride phases in trotting horses from video sequences under field conditions. Wien. Tierarztl. Mon. 2010;97:65–73.
  26. Starke SD, Clayton HM. A universal approach to determine footfall timings from kinematics of a single foot marker in hoofed animals. PeerJ 2015;3:e783.
    doi: 10.7717/peerj.783pmc: PMC4493675pubmed: 26157641google scholar: lookup
  27. Bosch S, Serra Bragança F, Marin-Perianu M, Marin-Perianu R, van der Zwaag B, Voskamp J, Back W, van Weeren R, Havinga P. EquiMoves: A Wireless Networked Inertial Measurement System for Objective Examination of Horse Gait. Sensors 2018;18:850.
    doi: 10.3390/s18030850pmc: PMC5877382pubmed: 29534022google scholar: lookup
  28. Pfau T, Starke SD, Tröster S, Roepstorff L. Estimation of vertical tuber coxae movement in the horse from a single inertial measurement unit. Vet. J. 2013;198:498–503.
    doi: 10.1016/j.tvjl.2013.09.005pubmed: 24268482google scholar: lookup
  29. Moorman VJ, Reiser II RF, McIlwraith CW, Kawcak CE. Validation of an equine inertial measurement unit system in clinically normal horses during walking and trotting. Am. J. Vet. Res. 2012;73:1160–1170.
    doi: 10.2460/ajvr.73.8.1160pubmed: 22849676google scholar: lookup
  30. Cruz AM, Maninchedda UE, Burger D, Wanda S, Vidondo B. Repeatability of gait pattern variables measured by use of extremity-mounted inertial measurement units in nonlame horses during trotting. Am. J. Vet. Res. 2017;78:1011–1018.
    doi: 10.2460/ajvr.78.9.1011pubmed: 28836845google scholar: lookup
  31. Bragança FM, Bosch S, Voskamp JP, Marin-Perianu M, Van der Zwaag BJ, Vernooij JCM, van Weeren PR, Back W. Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot. Equine Vet. J. 2017;49:545–551.
    doi: 10.1111/evj.12651pmc: PMC5484301pubmed: 27862238google scholar: lookup
  32. Robert C, Valette JP, Denoix JM. The effects of treadmill inclination and speed on the activity of two hindlimb muscles in the trotting horse. Equine Vet. J. 2000;32:312–317.
    doi: 10.2746/042516400777032246pubmed: 10952380google scholar: lookup
  33. Robert C, Valette JP, Pourcelot P, Audigie F, Denoix JM. Effects of trotting speed on muscle activity and kinematics in saddlehorses. Equine Vet. J. 2002;34:295–301.
    doi: 10.1111/j.2042-3306.2002.tb05436.xpubmed: 12405704google scholar: lookup
  34. Mallat SG. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989;11:674–693.
    doi: 10.1109/34.192463google scholar: lookup
  35. Jobert M, Tismer C, Poiseau E, Schulz H. Wavelets—A new tool in sleep biosignal analysis. J. Sleep Res. 1994;3:223–232.
    doi: 10.1111/j.1365-2869.1994.tb00135.xpubmed: 10607129google scholar: lookup
  36. Pang Y, Christenson J, Jiang F, Lei T, Rhoades R, Kern D, Thompson JA, Liu C. Automatic detection and quantification of hand movements toward development of an objective assessment of tremor and bradykinesia in Parkinson’s disease. J. Neurosci. Methods. 2020;333:108576.
    doi: 10.1016/j.jneumeth.2019.108576pubmed: 31923452google scholar: lookup
  37. Bland JM, Altman DG. Agreement between Methods of Measurement with Multiple Observations Per Individual. J. Biopharm. Stat. 2007;17:571–582.
    doi: 10.1080/10543400701329422pubmed: 17613642google scholar: lookup
  38. Nez A, Fradet L, Laguillaumie P, Monnet T, Lacouture P. Simple and efficient thermal calibration for MEMS gyroscopes. Med. Eng. Phys. 2018;55:60–67.
    doi: 10.1016/j.medengphy.2018.03.002pubmed: 29576459google scholar: lookup
  39. Lepetit K, Ben Mansour K, Boudaoud S, Kinugawa-Bourron K, Marin F. Evaluation of the kinetic energy of the torso by magneto-inertial measurement unit during the sit-to-stand movement. J. Biomech. 2018;67:172–176.
    doi: 10.1016/j.jbiomech.2017.11.028pubmed: 29269002google scholar: lookup
  40. Verlinde D, Beckers F, Ramaekers D, Aubert AE. Wavelet decomposition analysis of heart rate variability in aerobic athletes. Auton. Neurosci. 2001;90:138–141.
    doi: 10.1016/S1566-0702(01)00284-3pubmed: 11485282google scholar: lookup
  41. Soangra R, Lockhart TE, Van de Berge N. An approach for identifying gait events using wavelet denoising technique and single wireless IMU. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2011;55:1990–1994.
    doi: 10.1177/1071181311551415google scholar: lookup
  42. Park JS, Lee SW, Park U. R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope. J. Healthc. Eng. 2017;2017:4901017.
    doi: 10.1155/2017/4901017pmc: PMC5516746pubmed: 29065613google scholar: lookup
  43. Benson L, Clermont C, Watari R, Exley T, Ferber R. Automated Accelerometer-Based Gait Event Detection during Multiple Running Conditions. Sensors 2019;19:1483.
    doi: 10.3390/s19071483pmc: PMC6480623pubmed: 30934672google scholar: lookup
  44. Boutaayamou M, Schwartz C, Stamatakis J, Denoël V, Maquet D, Forthomme B, Croisier JL, Macq B, Verly JG, Garraux G. Development and validation of an accelerometer-based method for quantifying gait events. Med. Eng. Phys. 2015;37:226–232.
    doi: 10.1016/j.medengphy.2015.01.001pubmed: 25618221google scholar: lookup
  45. O’Connor CM, Thorpe SK, O’Malley MJ, Vaughan CL. Automatic detection of gait events using kinematic data. Gait Posture 2007;25:469–474.
    doi: 10.1016/j.gaitpost.2006.05.016pubmed: 16876414google scholar: lookup
  46. Hernlund E, Egenvall A, Peterson ML, Mahaffey CA, Roepstorff L. Hoof accelerations at hoof-surface impact for stride types and functional limb types relevant to show jumping horses. Vet. J. 2013;198:e27–e32.
    doi: 10.1016/j.tvjl.2013.09.029pubmed: 24511635google scholar: lookup
  47. Boyer KA, Nigg BM. Soft tissue vibrations within one soft tissue compartment. J. Biomech. 2006;39:645–651.
    doi: 10.1016/j.jbiomech.2005.01.027pubmed: 16439234google scholar: lookup
  48. Pacini Panebianco G, Bisi MC, Stagni R, Fantozzi S. Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements. Gait Posture 2018;66:76–82.
    doi: 10.1016/j.gaitpost.2018.08.025pubmed: 30170137google scholar: lookup
  49. Back W, Schamhardt HC, Savelberg HHCM, Bogert AJ, Bruin G, Hartman W, Barneveld A. How the horse moves: 1. Significance of graphical representations of equine forelimb kinematics. Equine Vet. J. 1995;27:31–38.
    doi: 10.1111/j.2042-3306.1995.tb03029.xpubmed: 7774544google scholar: lookup
  50. Clayton HM. The effect of an acute hoof wall angulation on the stride kinematics of trotting horses. Equine Vet. J. 1990;22:86–90.
    doi: 10.1111/j.2042-3306.1990.tb04742.xpubmed: 9259814google scholar: lookup
  51. Cano MR, Vivo J, MirÓ F, Morales JL, Galisteo AM. Kinematic characteristics of Andalusian, Arabian and Anglo-Arabian horses: A comparative study. Res. Vet. Sci. 2001;71:147–153.
    doi: 10.1053/rvsc.2001.0504pubmed: 11883894google scholar: lookup
  52. Chateau H, Degueurce C, Denoix JM. Three-dimensional kinematics of the distal forelimb in horses trotting on a treadmill and effects of elevation of heel and toe. Equine Vet. J. 2010;38:164–169.
    doi: 10.2746/042516406776563260pubmed: 16536387google scholar: lookup
  53. Buchner HHF, Savelberg HHCM, Schamhardt HC, Merkens HW, Barneveld A. Kinematics of treadmill versus overground locomotion in horses. Vet. Q. 1994;16:87–90.
    doi: 10.1080/01652176.1994.9694509pubmed: 7801509google scholar: lookup
  54. Rezvanian S, Lockhart T. Towards Real-Time Detection of Freezing of Gait Using Wavelet Transform on Wireless Accelerometer Data. Sensors 2016;16:475.
    doi: 10.3390/s16040475pmc: PMC4850989pubmed: 27049389google scholar: lookup

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    doi: 10.3390/s25154712pubmed: 40807876google scholar: lookup
  2. Fercher C, Bartsch J, Kluge S, Schneider F, Liedtke AM, Schleichardt A, Ueberschär O. Applying Multi-Purpose Commercial Inertial Sensors for Monitoring Equine Locomotion in Equestrian Training. Sensors (Basel) 2024 Dec 21;24(24).
    doi: 10.3390/s24248170pubmed: 39771905google scholar: lookup
  3. Hatrisse C, Macaire C, Hebert C, Hanne-Poujade S, De Azevedo E, Audigié F, Ben Mansour K, Marin F, Martin P, Mezghani N, Chateau H, Chèze L. A Method for Quantifying Back Flexion/Extension from Three Inertial Measurement Units Mounted on a Horse's Withers, Thoracolumbar Region, and Pelvis. Sensors (Basel) 2023 Dec 5;23(24).
    doi: 10.3390/s23249625pubmed: 38139471google scholar: lookup
  4. Giraudet C, Moiroud C, Beaumont A, Gaulmin P, Hatrisse C, Azevedo E, Denoix JM, Ben Mansour K, Martin P, Audigié F, Chateau H, Marin F. Development of a Methodology for Low-Cost 3D Underwater Motion Capture: Application to the Biomechanics of Horse Swimming. Sensors (Basel) 2023 Oct 30;23(21).
    doi: 10.3390/s23218832pubmed: 37960531google scholar: lookup
  5. Crecan CM, Peștean CP. Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review. Sensors (Basel) 2023 Jul 11;23(14).
    doi: 10.3390/s23146301pubmed: 37514599google scholar: lookup
  6. Timmerman I, Macaire C, Hanne-Poujade S, Bertoni L, Martin P, Marin F, Chateau H. A Pilot Study on the Inter-Operator Reproducibility of a Wireless Sensors-Based System for Quantifying Gait Asymmetries in Horses. Sensors (Basel) 2022 Dec 6;22(23).
    doi: 10.3390/s22239533pubmed: 36502233google scholar: lookup
  7. Fischer D, Friebel LIG, Grund S, Winter W, Wagner FC, Mülling CKW. Gait Analysis in Walking and Trotting Dairy Cows on Different Flooring Types with Novel Mobile Pressure Sensors and Inertial Sensors. Animals (Basel) 2022 Sep 16;12(18).
    doi: 10.3390/ani12182457pubmed: 36139317google scholar: lookup
  8. Domino M, Borowska M, Zdrojkowski Ł, Jasiński T, Sikorska U, Skibniewski M, Maśko M. Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding. Sensors (Basel) 2022 Aug 13;22(16).
    doi: 10.3390/s22166052pubmed: 36015813google scholar: lookup
  9. Pasquiet B, Biau S, Trébot Q, Debril JF, Durand F, Fradet L. Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept. Sensors (Basel) 2022 Jul 1;22(13).
    doi: 10.3390/s22134981pubmed: 35808476google scholar: lookup
  10. Hatrisse C, Macaire C, Sapone M, Hebert C, Hanne-Poujade S, De Azevedo E, Marin F, Martin P, Chateau H. Stance Phase Detection by Inertial Measurement Unit Placed on the Metacarpus of Horses Trotting on Hard and Soft Straight Lines and Circles. Sensors (Basel) 2022 Jan 18;22(3).
    doi: 10.3390/s22030703pubmed: 35161452google scholar: lookup
  11. Briggs EV, Mazzà C. Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass. PLoS One 2021;16(7):e0254813.
    doi: 10.1371/journal.pone.0254813pubmed: 34310630google scholar: lookup
  12. Steinke SL, Montgomery JB, Barden JM. Accelerometry-Based Step Count Validation for Horse Movement Analysis During Stall Confinement. Front Vet Sci 2021;8:681213.
    doi: 10.3389/fvets.2021.681213pubmed: 34239913google scholar: lookup
  13. Sapone M, Martin P, Ben Mansour K, Chateau H, Marin F. The Protraction and Retraction Angles of Horse Limbs: An Estimation during Trotting Using Inertial Sensors. Sensors (Basel) 2021 May 30;21(11).
    doi: 10.3390/s21113792pubmed: 34070859google scholar: lookup
  14. Marin F. Human and Animal Motion Tracking Using Inertial Sensors. Sensors (Basel) 2020 Oct 26;20(21).
    doi: 10.3390/s20216074pubmed: 33114597google scholar: lookup
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