Machine learning is a branch of artificial intelligence that involves the development of algorithms and statistical models enabling computers to perform tasks without explicit instructions. In the context of equine research, machine learning can be applied to analyze large datasets related to horse health, behavior, and performance. It can assist in identifying patterns and correlations that may not be immediately apparent through traditional analysis methods. Applications of machine learning in equine studies include predicting disease outbreaks, assessing gait abnormalities, and optimizing breeding strategies. This page compiles peer-reviewed research studies and scholarly articles that explore the applications, methodologies, and outcomes of using machine learning techniques in the study of equine science.
Schmutz A, Chèze L, Jacques J, Martin P.With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or the sports one to estimate spatiotemporal parameters. The purpose of the present study was to develop a model that can be included in a smart device in order to estimate the horse speed per stride from accelerometric and gyroscopic data without the use of a global positioning system, enabling the use of such a tool in both indoor and outdoor condi...
Putnová L, Štohl R.Considering the extensive data sets and statistical techniques, animal breeding embodies a branch of machine learning that has a constantly increasing impact on breeding. In our study, information regarding the potential of machine learning and data mining within a large set of horses and breeds is presented. The individual assignment methods and factors influencing the success rate of the procedure are compared at the Czech population scale. The fixation index values ranged from 0.057 (HMS1) to 0.144 (HTG6), and the overall genetic differentiation amounted to 8.9% among the breeds. The highes...
Dahl CD, Wyss C, Zuberbühler K, Bachmann I.One model of signal evolution is based on the notion that behaviours become increasingly detached from their original biological functions to obtain a communicative value. Selection may not always favour the evolution of such transitions, for instance, if signalling is costly due to predators usurping signal production. Here, we collected inertial movement sensing data recorded from multiple locations in free-ranging horses (Equus caballus), which we subjected to a machine learning algorithm to extract kinematic gestalt profiles. This yielded surprisingly rich and multi-layered sets of informa...
Lee JN, Lee MW, Byeon YH, Lee WS, Kwak KC.In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider's hip motion from the sensor information collected by inertial sensors as feature data for the cla...
Burocziova M, Riha J.Genetic relationships and population structure of 8 horse breeds in the Czech and Slovak Republics were investigated using classification methods for breed discrimination. To demonstrate genetic differences among these breeds, we used genetic information - genotype data of microsatellite markers and classification algorithms - to perform a probabilistic prediction of an individual's breed. In total, 932 unrelated animals were genotyped for 17 microsatellite markers recommended by the ISAG for parentage testing (AHT4, AHT5, ASB2, HMS3, HMS6, HMS7, HTG4, HTG10, VHL20, HTG6, HMS2, HTG7, ASB17, AS...
Schobesberger H, Peham C.The purpose of this study was to investigate whether artificial neural networks could be used to determine equine lameness by computational means only. The integral parts of our approach were the combination of automated signal tracking of horses on a treadmill and the computational power of artificial neural networks (ANN). The motion of 175 horses trotting on a treadmill was recorded using the SELSPOT II system for motion analysis. Two cameras traced infrared (IR) markers on the head and on the left forehoof. The motion of the head was Fourier-transformed and further processed by a multilaye...
Darbandi H, Serra Bragança F, van der Zwaag BJ, Voskamp J, Gmel AI, Haraldsdóttir EH, Havinga P.Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between bree...
Schobesberger H, Peham C.The purpose of this study was to investigate whether artificial neural networks could be used to determine equine lameness by computational means only. The integral parts of our approach were the combination of automated signal tracking of horses on a treadmill and the computational power of artificial neural networks (ANN). The motion of 175 horses trotting on a treadmill was recorded using the SELSPOT II system for motion analysis. Two cameras traced infrared (IR) markers on the head and on the left forehoof. The motion of the head was Fourier-transformed and further processed by a multilaye...
Davíðsson HB, Rees T, Ólafsdóttir MR, Einarsson H.Automated gait classification has traditionally been studied using horse-mounted sensors. However, smartphone-based sensors are more accessible, but the performance of gait classification models using data from such sensors has not been widely known or accessible. In this study, we performed horse gait classification using deep learning models and data from mobile phone sensors located in the rider's pocket. We gathered data from 17 horses and 14 riders. The data were gathered simultaneously from movement sensors in a mobile phone located in the rider's pocket and a gait classification system ...
ELKhamary AN, Keenihan EK, Schnabel LV, Redding WR, Schumacher J.While MRI is the modality of choice for the diagnosis of longitudinal tears (LTs) of the deep digital flexor tendon (DDFT) of horses, differentiating between various grades of tears based on imaging characteristics is challenging due to overlapping imaging features. In this retrospective, exploratory, diagnostic accuracy study, a machine learning (ML) scheme was applied to link quantitative features and qualitative descriptors to leverage MRI characteristics of different grades of tearing of the DDFT of horses. A qualitative MRI characteristic scheme, combining tendon morphologic features, alt...
Krishnan BS, Jones LR, Elmore JA, Samiappan S, Evans KO, Pfeiffer MB, Blackwell BF, Iglay RB.Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We cla...
Nissen SD, Saljic A, Carstensen H, Braunstein TH, Hesselkilde EM, Kjeldsen ST, Hopster-Iversen C, D'Souza A, Jespersen T, Buhl R.Second-degree atrioventricular (AV) block at rest is very common in horses. The underlying molecular mechanisms are unexplored, but commonly attributed to high vagal tone. Unassigned: To assess whether AV block in horses is due to altered expression of the effectors of vagal signalling in the AV node, with specific emphasis on the muscarinic acetylcholine receptor (M) and the G protein-gated inwardly rectifying K (GIRK4) channel that mediates the cardiac current. Unassigned: Eighteen horses with a low burden of second-degree AV block (median 8 block per 20 h, IQR: 32 per 20 h) were assign...
