The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

Monitoring disease progression in Parkinson’s disease is challenging. Postural transfers by sit-to-stand motions are adapted to trace the motor performance of subjects. Wearable sensors such as inertial measurement units allow for monitoring motion performance. We propose quantifying the sit-to-stand performance based on two scores compiling kinematics, dynamics, and energy-related variables. Three groups participated in this research: asymptomatic young participants (n = 33), senior asymptomatic participants (n = 17), and Parkinson’s patients (n = 20). An unsupervised classification was performed of the two scores to differentiate the three populations. We found a sensitivity of 0.4 and a specificity of 0.96 to distinguish Parkinson’s patients from asymptomatic subjects. In addition, seven Parkinson’s patients performed the sit-to-stand task “ON” and “OFF” medication, and we noted the scores improved with the patients’ medication states (MDS-UPDRS III scores). Our investigation revealed that Parkinson’s patients demonstrate a wide spectrum of mobility variations, and while one inertial measurement unit can quantify the sit-to-stand performance, differentiating between PD patients and healthy adults and distinguishing between “ON” and “OFF” periods in PD patients is still challenging.

Most techniques for evaluating unilateral impairments in facial movement yield subjective measurements. The objective of the present study was to define a reference dataset and develop a visualization tool for clinical assessments. In this prospective study, a motion capture system was used to quantify facial movements in 30 healthy adults and 2 patients. We analyzed the displacements of 105 reflective markers placed on the participant's face during five movements (M1–M5). For each marker, the primary endpoint was the maximum amplitude of displacement from the static position (M0) in an analysis of variance. The measurement precision was 0.1 mm. Significant displacements of markers were identified for M1–M5, and displacement patterns were defined. The patients and age‐matched healthy participants were compared with regard to the amplitude of displacement. We created a new type of radar plot to visually represent the diagnosis and facilitate effective communication between medical professionals. In proof‐of‐concept experiments, we collected quantitative data on patients with facial palsy and created a patient‐specific radar plot. Our new protocol for clinical facial motion capture (“quantified analysis of facial movement,” QAFM) was accurate and should thus facilitate the long‐term clinical follow‐up of patients with facial palsy. To take account of the limitations affecting the comparison with the healthy side, we created a dataset of healthy facial movements; our method might therefore be applicable to other conditions in which movements on one or both sides of the face are impaired. The patient‐specific radar plot enables clinicians to read and understand the results rapidly.

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.

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.