Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds.
Abstract: The shift of the horse breeding sector from agricultural to leisure and sports purposes led to a decrease in local breeds' population size due to the loss of their original breeding purposes. Most of the Italian breeds must adapt to modern market demands, and gait traits are suitable phenotypes to help this process. Inertial measurement unit (IMU) technology can be used to objectively assess them. This work aims to investigate on IMU recorded data (i) the influence of environmental factors and biometric measurements, (ii) their repeatability, (iii) the correlation with judge evaluations, and (iv) their predictive value. Unassigned: The Equisense Motion S was used to collect phenotypes on 135 horses, Bardigiano (101) and Murgese (34) and the data analysis was conducted using R (v.4.1.2). Analysis of variance (ANOVA) was employed to assess the effects of biometric measurements and environmental and animal factors on the traits. Unassigned: Variations in several traits depending on the breed were identified, highlighting different abilities among Bardigiano and Murgese horses. Repeatability of horse performance was assessed on a subset of horses, with regularity and elevation at walk being the traits with the highest repeatability (0.63 and 0.72). The positive correlation between judge evaluations and sensor data indicates judges' ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). A high variability was observed in the accuracy of the SVM model, ranging from 55 to 100% while the other two models showed higher consistency, with accuracy ranging from 74 to 100% for the GBM and from 64 to 88% for the KNN. Overall, the GBM model exhibits the highest accuracy and the lowest error. In conclusion, integrating IMU technology into horse performance evaluation offers valuable insights, with implications for breeding and training.
Copyright © 2024 Asti, Ablondi, Molle, Zanotti, Vasini and Sabbioni.
Publication Date: 2024-10-16 PubMed ID: 39479203PubMed Central: PMC11521968DOI: 10.3389/fvets.2024.1459553Google 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.
- 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 uses inertial measurement unit technology (IMU) to study gait traits in two breeds of Italian horses – Bardigiano and Murgese. The study aims to understand environmental and biometric influences, repeatability of data, correlation with judge evaluations, and their predictive value.
Study Design and Methods
- The researchers used The Equisense Motion S to collect data from 135 horses comprising 101 Bardigiano and 34 Murgese.
- Data analysis was conducted using the R software
- The team used analysis of variance (ANOVA) to assess the effects of environmental factors, animal factors, and biometric measurements on the horse gait traits.
Results
- Differences were identified in various traits depending on the breed, highlighting varying abilities among Bardigiano and Murgese horses.
- The study found that regularity and elevation at walk had the highest repeatability, with scores of 0.63 and 0.72 respectively.
- A positive correlation between judge evaluations and sensor data indicates the judges’ ability to evaluate overall gait quality accurately.
- Three different algorithms—Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN)—were used to predict judges’ scores from the IMU measurements.
- While the accuracy of the SVM model varied greatly, GBM and KNN models showed higher consistency. Of all three, GBM had the highest accuracy and lowest error.
Conclusions
- The study concluded that integrating IMU technology into horse performance evaluation provides valuable insights, which could significantly affect breeding and training decisions.
- Despite the shift from agricultural to leisure and sports purposes, local horse breeds can adopt modern market demands, with gait traits as suitable phenotypes to assist this process.
- The research findings indicate that IMU technology is a reliable tool for aiding in the preservation and utilisation of local horse breeds.
Cite This Article
APA
Asti V, Ablondi M, Molle A, Zanotti A, Vasini M, Sabbioni A.
(2024).
Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds.
Front Vet Sci, 11, 1459553.
https://doi.org/10.3389/fvets.2024.1459553 Publication
Researcher Affiliations
- Department of Veterinary Sciences, University of Parma, Parma, Italy.
- Department of Veterinary Sciences, University of Parma, Parma, Italy.
- Department of Veterinary Sciences, University of Parma, Parma, Italy.
- Department of Veterinary Sciences, University of Parma, Parma, Italy.
- Italian Breeding Association for Equine and Donkey Breeds (ANAREAI), Roma, Italy.
- Department of Veterinary Sciences, University of Parma, Parma, Italy.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
This article includes 33 references
- Bélanger J, Pilling D. The state of the World’s biodiversity for food and agriculture.. Rome: FAO; (2019).
- Crossman GK, Walsh R. The changing role of the horse: from beast of burden to partner in sport and recreation.. Int J Sport Soc (2011) 2:95–110.
- FAO. Status and trends of animal genetic resources.. (2022).
- Capomaccio S, Ablondi M, Colombi D, Sartori C, Giontella A, Cappelli K. Exploring the Italian equine gene pool via high-throughput genotyping.. Front Genet (2023) 14:1099896.
- Ablondi M, Vasini M, Beretti V, Superchi P, Sabbioni A. Exploring genetic diversity in an Italian horse native breed to develop strategies for preservation and management.. J Anim Breed Genet (2018) 135:450–9.
- Ablondi M, Dadousis C, Vasini M, Eriksson S, Mikko S, Sabbioni A. Genetic diversity and signatures of selection in a native Italian horse breed based on SNP data.. Anim Open Access J MDPI (2020) 10:1005.
- Ablondi M, Summer A, Vasini M, Simoni M, Sabbioni A. Genetic parameters estimation in an Italian horse native breed to support the conversion from agricultural uses to riding purposes.. J Anim Breed Genet (2020) 137:200–10.
- Gille C, Kayser M, Spiller A. Target group segmentation in the horse buyers’ market against the background of equestrian experience.. J Equine Sci (2010) 21:67–71.
- Pérez-Enciso M, Steibel JP. Phenomes: the current frontier in animal breeding.. Genet Sel Evol (2021) 53:22.
- McCoy AM, Beeson SK, Rubin C-J, Andersson L, Caputo P, Lykkjen S. Identification and validation of genetic variants predictive of gait in standardbred horses.. PLoS Genet (2019) 15:e1008146.
- Hobbs SJ, Serra Braganca FM, Rhodin M, Hernlund E, Peterson M, Clayton HM. Evaluating overall performance in high-level dressage horse-rider combinations by comparing measurements from inertial sensors with general impression scores awarded by judges.. Animals (2023) 13:2496.
- Rana M, Mittal V. Horse gait analysis using wearable inertial sensors and machine learning.. Proc Inst Mech Eng Part P J Sports Eng Technol (2023) 5:17543371231196814.
- Yigit T, Han F, Rankins E, Yi J, McKeever KH, Malinowski K. Wearable inertial sensor-based limb lameness detection and pose estimation for horses.. IEEE Trans Autom Sci Eng (2022) 19:1365–79.
- 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) 47:1220–4.
- Halachmi I, Guarino M, Bewley J, Pastell M. Smart animal agriculture: application of real-time sensors to improve animal well-being and production.. Annu Rev Anim Biosci (2019) 7:403–25.
- Brighton C, Olsen E, Pfau T. Is a standalone inertial measurement unit accurate and precise enough for quantification of movement symmetry in the horse?. Comput Methods Biomech Biomed Engin (2015) 18:527–32.
- Neethirajan S. The role of sensors, big data and machine learning in modern animal farming.. Sens Bio Sens Res (2020) 29:100367.
- Bosch S, Braganca F, Marin-Perianu M, Marin-Perianu R, van der Zwaag BJ, Voskamp J. EquiMoves: a wireless networked inertial measurement system for objective examination of horse gait.. Sensors (2018) 18:850.
- Gmel AI, Haraldsdóttir EH, Serra Bragança FM, Cruz AM, Neuditschko M, Weishaupt MA. Determining objective parameters to assess gait quality in Franches-Montagnes horses for ground coverage and over-tracking - part 1: at walk.. J Equine Vet (2022) 115:104024.
- Leelamankong P, Estrada R, Mählmann K, Rungsri P, Lischer C. Agreement among equine veterinarians and between equine veterinarians and inertial sensor system during clinical examination of hindlimb lameness in horses.. Equine Vet J (2020) 52:326–31.
- Hardeman AM, Egenvall A, Serra Bragança FM, Swagemakers J-H, Koene MHW, Roepstorff L. Visual lameness assessment in comparison to quantitative gait analysis data in horses.. Equine Vet J (2022) 54:1076–85.
- Crecan CM, Peștean CP. Inertial sensor technologies—their role in equine gait analysis, a review.. Sensors (2023) 23:6301.
- Stoffel MA, Nakagawa S, Schielzeth H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models.. Methods Ecol Evol (2017) 8:1639–44.
- Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines.. IEEE Intell Syst Their Appl (1998) 13:18–28.
- Friedman JH. Greedy function approximation: a gradient boosting machine.. Ann Stat (2001) 29:1189–232.
- Guo G, Wang H, Bell D, Bi Y, Greer K. KNN model-based approach in classification. In: Meersman R, Tari Z, Schmidt DC, editors. On the move to meaningful internet systems 2003: CoopIS, DOA, and ODBASE. Berlin: Springer; (2003). 986–96.
- Rainio O, Teuho J, Klén R. Evaluation metrics and statistical tests for machine learning.. Sci Rep (2024) 14:6086.
- Panagiotopoulou O, Rankin JW, Gatesy SM, Hutchinson JR. A preliminary case study of the effect of shoe-wearing on the biomechanics of a horse’s foot.. PeerJ (2016) 4:e2164.
- Stutz JC, Vidondo B, Ramseyer A, Maninchedda UE, Cruz AM. Effect of three types of horseshoes and unshod feet on selected non-podal forelimb kinematic variables measured by an extremity mounted inertial measurement unit sensor system in sound horses at the trot under conditions of treadmill and soft geotextile surface exercise.. Vet Rec Open (2018) 5:e000237.
- Back W, Clayton HM. Equine Locomotion.. London: Elsevier Health Sciences; (2013).
- Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: applications, challenges and trends.. Neurocomputing (2020) 408:189–215.
- Yong Z, Youwen L, Shixiong X. An improved KNN text classification algorithm based on clustering.. J Comput (2009) 4:237.
- Zhang S, Li X, Zong M, Zhu X, Wang R. Efficient kNN classification with different numbers of nearest neighbors.. IEEE Trans Neural Netw Learn Syst (2018) 29:1774–85.
Citations
This article has been cited 0 times.Use Nutrition Calculator
Check if your horse's diet meets their nutrition requirements with our easy-to-use tool Check your horse's diet with our easy-to-use tool
Talk to a Nutritionist
Discuss your horse's feeding plan with our experts over a free phone consultation Discuss your horse's diet over a phone consultation
Submit Diet Evaluation
Get a customized feeding plan for your horse formulated by our equine nutritionists Get a custom feeding plan formulated by our nutritionists
