Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept

Pasquiet, Benoît; Biau, Sophie; Trébot, Quentin; Debril, Jean-François; Durand, François; Fradet, Laetitia

doi:10.3390/s22134981

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…By monitoring specific accelerometric variables, this observational exploratory field study identified several kinematic changes in eventing horses between and at the jumps across 4*‐cross‐country tests. This complements two recent studies using another inertial measurement unit and examining sport horses before and after their training sessions 15,21 . New candidates for kinematic characteristics during competitive exercise, such as the maximal strike power at the jumps, were identified in our study to be associated with an altered jumping profile and potentially indicating the onset of fatigue.…”

Section: Discussionsupporting

confidence: 65%

“…This complements two recent studies using another inertial measurement unit and examining sport horses before and after their training sessions. 15,21 New candidates for kinematic characteristics during competitive exercise, such as the maximal strike power at the jumps, were identified in our study to be associated with an altered jumping profile and potentially indicating the onset of fatigue.…”

Section: Discussionmentioning

confidence: 87%

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High‐level competition exercise and related fatigue are associated with stride and jumping characteristics in eventing horses

Burger,

Vidondo,

Gerber

et al. 2023

Equine Veterinary Journal

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BackgroundFatigue and related injuries to the musculoskeletal system are among the most frequent reasons for the withdrawal of high‐level eventing horses from the sport. The safety of both horse and rider is very important, and early detection of fatigue is crucial.ObjectivesTo investigate elite eventing horses in competitive events focusing on biomechanical, cardiovascular and metabolic variables across the cross‐country test and to identify their potential associations with fatigue.Study designProspective observational exploratory field study.MethodsObservations on 54 cross‐country tests of 33 horses at five competitive, high‐level events were evaluated using sternal accelerometric analysis of stride parameters between and at the jumps. Blood lactate concentration and heart rate were determined 10 min after finishing. The differences in kinematic parameters between the course start and end were analysed with mixed models for repeated measures. Associations between blood lactate and heart rate recovery with the kinematic variables were quantified with Pearson correlation coefficients.ResultsWe observed numerous stride characteristics between the jumps and the jumps changing over time during the courses. Blood lactate concentrations were positively correlated with the mean maximal strike power at the jumps in the last minute of the course (r = 0.41; p < 0.001), and the latter was negatively correlated with the mean stride height over the jumps (r = −0.41; p = 0.003).Main limitationsThe sample contained horses of varying breeds, sexes and ages, and different horses participated in different events.ConclusionsWe identified several kinematic changes during a cross‐country test depending on event, speed and fatigue.

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Section: Discussionsupporting

confidence: 65%

Section: Discussionmentioning

confidence: 87%

High‐level competition exercise and related fatigue are associated with stride and jumping characteristics in eventing horses

Burger,

Vidondo,

Gerber

et al. 2023

Equine Veterinary Journal

View full text Add to dashboard Cite

show abstract

“…Locomotion pattern modifications were detected before and after training sessions in horses. Among the kinematic indicators evaluated, indicators comparing gait cycle kinematics (Pearson’s correlation coefficients and root mean square deviations) and estimating movement smoothness (spectral arc length) indicated changes following training [ 87 ].…”

Section: Horse–rider Interaction and Imusmentioning

confidence: 99%

Inertial Sensor Technologies—Their Role in Equine Gait Analysis, a Review

Crecan

Peștean

2023

Sensors

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Objective gait analysis provides valuable information about the locomotion characteristics of sound and lame horses. Due to their high accuracy and sensitivity, inertial measurement units (IMUs) have gained popularity over objective measurement techniques such as force plates and optical motion capture (OMC) systems. IMUs are wearable sensors that measure acceleration forces and angular velocities, providing the possibility of a non-invasive and continuous monitoring of horse gait during walk, trot, or canter during field conditions. The present narrative review aimed to describe the inertial sensor technologies and summarize their role in equine gait analysis. The literature was searched using general terms related to inertial sensors and their applicability, gait analysis methods, and lameness evaluation. The efficacy and performance of IMU-based methods for the assessment of normal gait, detection of lameness, analysis of horse–rider interaction, as well as the influence of sedative drugs, are discussed and compared with force plate and OMC techniques. The collected evidence indicated that IMU-based sensor systems can monitor and quantify horse locomotion with high accuracy and precision, having comparable or superior performance to objective measurement techniques. IMUs are reliable tools for the evaluation of horse–rider interactions. The observed efficacy and performance of IMU systems in equine gait analysis warrant further research in this population, with special focus on the potential implementation of novel techniques described and validated in humans.

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Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors

Davíðsson

Rees²,

Ólafsdóttir

et al. 2023

Animals

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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 based on four wearable sensors attached to the horse’s limbs. With this efficient approach to acquire labelled data, we trained a Bi-LSTM model for gait classification. The only input to the model was a 50 Hz signal from the phone’s accelerometer and gyroscope that was rotated to the horse’s frame of reference. We demonstrate that sensor data from mobile phones can be used to classify the five gaits of the Icelandic horse with up to 94.4% accuracy. The result suggests that horse riding activities can be studied at a large scale using mobile phones to gather data on gaits. While our study showed that mobile phone sensors could be effective for gait classification, there are still some limitations that need to be addressed in future research. For example, further studies could explore the effects of different riding styles or equipment on gait classification accuracy or investigate ways to minimize the influence of factors such as phone placement. By addressing these questions, we can continue to improve our understanding of horse gait and its role in horse riding activities.

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Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept

Cited by 3 publications

References 44 publications

High‐level competition exercise and related fatigue are associated with stride and jumping characteristics in eventing horses

High‐level competition exercise and related fatigue are associated with stride and jumping characteristics in eventing horses

Inertial Sensor Technologies—Their Role in Equine Gait Analysis, a Review

Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors

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