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Equine swimming is increasingly used for injury prevention and rehabilitation, but objective analysis of movement during swimming remains limited compared to land-based locomotion. Spatiotemporal parameters are essential for evaluating therapeutic outcomes, yet capturing these parameters is technically challenging due to difficulties in observing limb motion in water. Inertial sensors, already widely applied in equine science, offer a promising solution for measuring swimming kinematics objectively. The objective of this study was to evaluate the reliability of inertial sensors placed on equine distal limbs in detecting key spatiotemporal events during swimming by comparing it with video-based detection made by veterinarians. For the duration of the hindlimb swimming cycle, 24 data values were analysed and showed an “excellent” agreement, with an intraclass correlation coefficient = 0.96, 95% CI: 0.904–0.983, and Bland–Altmann analysis showed an upper limit of agreement of 50 ms (95% CI: 70 ms, 30 ms) and lower one of −60 ms (95% CI: −40 ms, −80 ms). The estimates of the “swimming” duty factor of the hindlimb (n = 24) demonstrated “moderate” to “excellent” with intraclass correlation of 0.82 (95% CI: 0.625–0.920) and limits of agreement of 4.39% (95% CI: 6.21%, 2.53%) and −5.28% (95% CI: −3.42%, −7.14%). The results of the forelimb were mixed, suggesting that the cycle duration and “swimming” duty factor parameters determined for this limb should be used with caution. Overall, the findings confirm that inertial sensors, particularly on the hindlimbs, provide reliable spatiotemporal measurements and are well suited for studying equine swimming.

Hydrotherapy has been utilized in horse rehabilitation programs for over four decades. However, a comprehensive description of the swimming cycle of horses is still lacking. One of the challenges in studying this motion is 3D underwater motion capture, which holds potential not only for understanding equine locomotion but also for enhancing human swimming performance. In this study, a marker-based system that combines underwater cameras and markers drawn on horses is developed. This system enables the reconstruction of the 3D motion of the front and hind limbs of six horses throughout an entire swimming cycle, with a total of twelve recordings. The procedures for pre- and post-processing the videos are described in detail, along with an assessment of the estimated error. This study estimates the reconstruction error on a checkerboard and computes an estimated error of less than 10 mm for segments of tens of centimeters and less than 1 degree for angles of tens of degrees. This study computes the 3D joint angles of the front limbs (shoulder, elbow, carpus, and front fetlock) and hind limbs (hip, stifle, tarsus, and hind fetlock) during a complete swimming cycle for the six horses. The ranges of motion observed are as follows: shoulder: 17 ± 3°; elbow: 76 ± 11°; carpus: 99 ± 10°; front fetlock: 68 ± 12°; hip: 39 ± 3°; stifle: 68 ± 7°; tarsus: 99 ± 6°; hind fetlock: 94 ± 8°. By comparing the joint angles during a swimming cycle to those observed during classical gaits, this study reveals a greater range of motion (ROM) for most joints during swimming, except for the front and hind fetlocks. This larger ROM is usually achieved through a larger maximal flexion angle (smaller minimal angle of the joints). Finally, the versatility of the system allows us to imagine applications outside the scope of horses, including other large animals and even humans.

Back mobility is a criterion of well-being in a horse. Veterinarians visually assess the mobility of a horse’s back during a locomotor examination. Quantifying it with on-board technology could be a major breakthrough to help them. The aim of this study was to evaluate the accuracy of a method of quantifying the back mobility of horses from inertial measurement units (IMUs) compared to motion capture (MOCAP) as a gold standard. Reflective markers and IMUs were positioned on the withers, eighteenth thoracic vertebra, and pelvis of four sound horses. The horses performed a walk and trot in straight lines and performed a gallop in circles on a soft surface. The developed method, based on the three IMUs, consists of calculating the flexion/extension angle of the thoracolumbar region. The IMU method showed a mean bias of 0.8° (±1.5°) (mean (±SD)) and 0.8° (±1.4°), respectively, for the flexion and extension movements, all gaits combined, compared to the MOCAP method. The results of this study suggest that the developed method has a similar accuracy to that of MOCAP, opening up possibilities for easy measurements under field conditions. Future studies will need to examine the correlations between these biomechanical measures and clinicians’ visual assessment of back mobility defects.

Repeatability and reproducibility of any measuring system must be evaluated to assess possible limitations for its use. The objective of this study was to establish the repeatability and the inter-operator reproducibility of a sensors-based system (EQUISYM®) for quantifying gait asymmetries in horses.. Seven wireless IMUs were placed on the head, the withers, the pelvis, and the 4 cannon bones on three horses, by four different operators, four times on each horse, which led to a total of 48 repetitions randomly assigned. Data were collected along three consecutive days and analysed to calculate total variance, standard deviation and the variance attributable to the operator on multiple asymmetry variables. Maximal percentage of variance due to the operator (calculated out of the total variance) was 5.3% and was related to the sensor placed on the head. The results suggest a good reproducibility of IMU-based gait analysis systems for different operators repositioning the system and repeating the same measurements at a succession of time intervals. Future studies will be useful to confirm that inter-operator reproducibility remains valid in larger groups and on horses with different degrees of locomotor asymmetry.

The development of on-board technologies has enabled the development of quantification systems to monitor equine locomotion parameters. Their relevance among others relies on their ability to determine specific locomotor events such as foot-on and heel-off events. The objective of this study was to compare the accuracy of different methods for an automatic gait events detection from inertial measurement units (IMUs). IMUs were positioned on the cannon bone, hooves, and withers of seven horses trotting on hard and soft straight lines and circles. Longitudinal acceleration and angular velocity around the latero-medial axis of the cannon bone, and withers dorso-ventral displacement data were identified to tag the foot-on and a heel-off events. The results were compared with a reference method based on hoof-mounted-IMU data. The developed method showed bias less than 1.79%, 1.46%, 3.45% and −1.94% of stride duration, respectively, for forelimb foot-on and heel-off, and for hindlimb foot-on and heel-off detection, compared to our reference method. The results of this study showed that the developed gait-events detection method had a similar accuracy to other methods developed for straight line analysis and extended this validation to other types of exercise (circles) and ground surface (soft surface).

Lameness detection in horses is a critical challenge in equine veterinary practice, particularly when symptoms are mild. This study aimed to develop a predictive system using a support vector machine (SVM) to identify the affected limb in horses trotting in a straight line. The system analyzed data from inertial measurement units (IMUs) placed on the horse’s head, withers, and pelvis, using variables such as vertical displacement and retraction angles. A total of 287 horses were included, with 256 showing single-limb lameness and 31 classified as sound. The model achieved an overall accuracy of 86%, with the highest success rates in identifying right and left forelimb lameness. However, there were challenges in identifying sound horses, with a 54.8% accuracy rate, and misclassification between forelimb and hindlimb lameness occurred in some cases. The study highlighted the importance of specific variables, such as vertical head and withers displacement, for accurate classification. Future research should focus on refining the model, exploring deep learning methods, and reducing the number of sensors required, with the goal of integrating these systems into equestrian equipment for early detection of locomotor issues.

The protraction and retraction angles of horse limbs are important in the analysis of horse locomotion. This study explored two methods from an IMU positioned on the canon bone of eight horses to estimate these angles. Each method was based on a hypothesis in order to define the moment corresponding with the verticality of the canon bone: (i) the canon bone is in a vertical position at 50% of the stance phase or (ii) the verticality of the canon bone corresponds with the moment when the horse’s withers reach their lowest point. The measurements were carried out on a treadmill at a trot and compared with a standard gold method based on motion capture. For the measurement of the maximum protraction and retraction angles, method (i) had average biases (0.7° and 1.7°) less than method (ii) (−1.3° and 3.7°). For the measurement of the protraction and retraction angles during the stance phase, method (i) had average biases (4.1° and −3.3°) higher to method (ii) (2.1° and −1.3°). This study investigated the pros and cons of a generic method (i) vs. a specific method (ii) to determine the protraction and retraction angles of horse limbs by a single IMU.

Locomotor disorders involving the spine are a major cause of impaired performance and early retirement in sport horses. Swimming is increasingly incorporated into rehabilitation protocols, but its effects on spinal biomechanics remain poorly understood. This prospective study evaluated changes in thoracolumbar mobility in sixteen sport horses diagnosed with cervical or thoracolumbar axial musculoskeletal lesions over a 12-week rehabilitation program comprising 4 weeks of land-based training followed by 8 weeks during which swimming sessions were incorporated three times per week. Weekly measurements of thoracolumbar flexion–extension range of motion (ROM) were performed during straight-line trot on a hard surface using inertial measurement units attached to the withers, T18, and tubera sacrale. Group-level analyses revealed minimal changes across training phases: in horses with thoracolumbar lesions, mean ROM decreased slightly during the second month of aquatic training (−0.1° [95% CI −0.1; 0], Cohen’s d = 0.2), whereas no significant variation was detected in horses with cervical lesions. As the study did not include a control group, these temporal changes cannot be specifically attributed to swimming and should be interpreted as descriptive rather than causal. Individual trajectories showed heterogeneous patterns, but these were not consistent enough to alter the group-level interpretation. Overall, the findings suggest that thoracolumbar mobility remains relatively stable throughout this type of rehabilitation program, highlighting the importance of individualized monitoring rather than the expectation of a uniform biomechanical response.

Aquatic training has been integrated into equine rehabilitation and training programs for several decades. While the cardiovascular effects of this training have been explored in previous studies, limited research exists on the locomotor patterns exhibited during the swimming cycle. This study aimed to analyze three distinct swimming strategies, identified by veterinarians, based on the propulsion phases of each limb: (S1) two-beat cycle with lateral overlap, (S2) two-beat cycle with diagonal overlap, and (S3) four-beat cycle. 125 underwater videos from eleven horses accustomed to swimming were examined to quantify the differences in locomotor patterns between these strategies. Initially, a classifier was developed to categorize 125 video segments into four groups (CatA to CatD). The results demonstrated that these categories correspond to specific swimming strategies, with CatA aligning with S1, CatB with S2, and CatC and CatD representing variations of S3. This classification highlights that two key parameters, lateral and diagonal ratios, are indeed effective in distinguishing between the different swimming strategies. Additionally, coordination patterns were analyzed in relation to these swimming strategies. One of the primary findings is the variability in swimming strategies both within and between individual horses. While five horses consistently maintained the same strategy throughout their swimming sessions, six others exhibited variations in their strategy between laps. This suggests that factors such as swimming direction, pauses between laps, and fatigue may influence the selection of swimming strategy. This study offers new insights into the locomotor patterns of horses during aquatic training and has implications for enhancing the design of rehabilitation protocols.

Defining whether a gait asymmetry should be considered as lameness is challenging. Gait analysis systems now provide relatively accurate objective data, but their interpretation remains complex. Thresholds for discriminating between horses that are visually assessed as being lame or sound, as well as thresholds for locating the lame limb with precise sensitivity and specificity are essential for accurate interpretation of asymmetry measures. The goal of this study was to establish the thresholds of asymmetry indices having the best sensitivity and specificity to represent the visual single-limb lameness assessment made by expert veterinarians as part of their routine practice. Horses included in this study were evaluated for locomotor disorders at a clinic and equipped with the EQUISYM® system using inertial measurement unit (IMU) sensors. Visual evaluation by expert clinicians allocated horses into five groups: 49 sound, 62 left forelimb lame, 67 right forelimb lame, 23 left hindlimb lame, and 23 right hindlimb lame horses. 1/10 grade lame horses were excluded. Sensors placed on the head (_H), the withers (_W), and the pelvis (_P) provided vertical displacement. Relative difference of minimal (AI-min) and maximal (AI-max) altitudes, and of upward (AI-up) and downward (AI-down) amplitudes between right and left stance phases were calculated. Receiver operating characteristic (ROC) curves discriminating the sound horses from each lame limb group revealed the threshold of asymmetry indice associated with the best sensitivity and specificity. AI-up_W had the best ability to discriminate forelimb lame horses from sound horses with thresholds (left: −7%; right: +10%) whose sensitivity was greater than 84% and specificity greater than 88%. AI-up_P and AI-max_P discriminated hindlimb lame horses from sound horses with thresholds (left: −7%; right: +18% and left: −10%; right: +6%) whose sensitivity was greater than 78%, and specificity greater than 82%. Identified thresholds will enable the interpretation of quantitative data from lameness quantification systems. This study is mainly limited by the number of included horses and deserves further investigation with additional data, and similar studies on circles are warranted.