Comparison of speed-dependent time, force and spatial parameters between Franches-Montagnes and European Warmblood horses walking and trotting on a treadmill

Speed alterations affect many gait analysis parameters. How horses adapt to speed is relevant in many equestrian disciplines and may differ between breeds. This study described changes in gait parameters in 38 Warmblood (WB) and 24 Franches-Montagnes (FM) horses subjected to an incremental speed test at walk (1.35–2.05 m/s) and trot (3.25–5.5 m/s). Time, force and spatial parameters of each limb were measured with an instrumented treadmill and analysed with regression analysis using speed as the independent variable. With higher speeds, stride rate, length, over-tracking distance and vertical ground reaction forces increased while the impulses decreased. The parameters followed the same linear or polynomial regression curves independent of breed, while the slope (linear) or incurvation (polynomial) often differed significantly between breeds. Some differences between the breeds were associated with height and speed (e.g. stride length at walk), and would disappear when scaling the data. The main differences between the breeds seem to stem from the movement of the hind limbs, with the FM obtaining long over-tracking distances despite the shorter height at withers. Some parameters relevant to gait quality could be improved in the FM to resemble WB movement by strict selection using objective measurements systems

Terrain Type Detection for Smart Equine Gait Analysis Systems Using Inertial Sensors and Machine Learning

Lameness, limping due to pain, is a significant welfare issue for horses. Veterinarians typically evaluate horses on two terrain types (hard and soft, e.g., asphalt and sand) that are known to affect the observed degree of lameness based on the origin/location of the pain. In the past years, whole-body inertial measurement units (IMU)-based gait analysis systems were developed to support diagnostics and monitor locomotion changes over time. Movement direction and gait (walk, trot) are automatically labeled, resulting in smart and easy-to-use systems. However, terrain types are not detected, leading to information loss. In this work, we explored terrain classification tasks with equine IMU data and machine and deep learning. Using the data of 111 horses equipped with IMU sensors (withers, pelvis, front, and hind limbs), we compared different features-based (FT) and time-series-based (TS) classifiers (train-test ratio: 0.7-0.3). In order to reduce the computational costs of the future system, we also evaluated the performance (F1 score) of the classifiers with different sampling frequencies (10 to 200Hz) and different IMU combinations (body and limbs). Our Convolutional Neural Network models accurately classified terrain types with only one IMU placed on the front limb. Downsampling the signals led to similar results, thus enabling real-time applications