Is Markerless More or Less? Comparing a Smartphone Computer Vision Method for Equine Lameness Assessment to Multi-Camera Motion Capture

Abstract

Computer vision is a subcategory of artificial intelligence focused on extraction of information from images and video. It provides a compelling new means for objective orthopaedic gait assessment in horses using accessible hardware, such as a smartphone, for markerless motion analysis. This study aimed to explore the lameness assessment capacity of a smartphone single camera (SC) markerless computer vision application by comparing measurements of the vertical motion of the head and pelvis to an optical motion capture multi-camera (MC) system using skin attached reflective markers. Twenty-five horses were recorded with a smartphone (60 Hz) and a 13 camera MC-system (200 Hz) while trotting two times back and forth on a 30 m runway. The smartphone video was processed using artificial neural networks detecting the horse’s direction, action and motion of body segments. After filtering, the vertical displacement curves from the head and pelvis were synchronised between systems using cross-correlation. This rendered 655 and 404 matching stride segmented curves for the head and pelvis respectively. From the stride segmented vertical displacement signals, differences between the two minima (MinDiff) and the two maxima (MaxDiff) respectively per stride were compared between the systems. Trial mean difference between systems was 2.2 mm (range 0.0–8.7 mm) for head and 2.2 mm (range 0.0–6.5 mm) for pelvis. Within-trial standard deviations ranged between 3.1–28.1 mm for MC and between 3.6–26.2 mm for SC. The ease of use and good agreement with MC indicate that the SC application is a promising tool for detecting clinically relevant levels of asymmetry in horses, enabling frequent and convenient gait monitoring over time.

Artificial intelligence: Is it wizardry, witchcraft, or a helping hand for an equine veterinarian?

“…An interesting case of image analysis includes the use of image analysis techniques for locomotion (gait) classification. Due to the importance of early lameness detection, and the simplicity of the imaging-various gait analysis techniques, ranging from complex specialised camera setups 11 to smartphone cameras 12,13 were demonstrated to be fit for the purpose, with forelimb lameness detected with 100% sensitivity and specificity.…”

Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses

“…The objective of the study was to investigate the limits of agreement (LoA) [20,21] for movement symmetry measures without exact stride matching for a realistic estimation of between-system agreement. Based on the published results of repeatability studies [7,22,23], we hypothesized that non-stride-matched conditions would exceed previously published LoA values calculated in comparison with 3D optical motion capture [3,4,8]. In addition, we were also interested in investigating the possible influence of circular movement (on the lunge) for LoA values.…”

“…Movement symmetry analysis can be achieved with body-mounted IMUs (inertial measurement units) [5] or optical methods with cameras [6,7]. Most recently, an artificial intelligence (AI)-based markerless smartphone app (Sleip ® , Stockholm, Sweden) has been validated against an established marker-based 3D optical motion capture system [8]. The markerless smartphone approach appears to be particularly interesting for clinical practice as it requires minimal infrastructure, only requiring internet access and an optional tripod.…”

“…The markerless smartphone approach appears to be particularly interesting for clinical practice as it requires minimal infrastructure, only requiring internet access and an optional tripod. In addition to the validated straight-line locomotion [8], the app also allows for the analysis of data from other clinically relevant "in-field" exercises such as on the lunge (i.e., horses moving in circles) on different surfaces.…”

“…Thus, the aim of this study was to conduct an "in-field" comparison of two validated movement symmetry measurement systems, a multi-sensor IMU system [3,4] and a smartphone app [8] for markerless video analysis. The objective of the study was to investigate the limits of agreement (LoA) [20,21] for movement symmetry measures without exact stride matching for a realistic estimation of between-system agreement.…”