26 Detecting equine gait events in field conditions

Abstract

As interest in analyzing equine gait in the real world grows, so does need for quantitative methods validated under field conditions. Gait analysis is predicated on accurate and precise detection of gait events– hoof on and off. Several methods of detection have been proposed which use inertial measurement units (IMUs); the most accurate is based on hoof mounted IMUs but attachment to the hooves is not always convenient. The aim of this research was thus to propose and test a novel gait event detection method using pastern mounted IMUs. Previous studies only focused on one hard surface; asphalt, grass and sand were investigated here. Eight geldings and 3 mares (mean(SD) height 154(21)cm and age 12(8) yr) including Irish Sport Horses, Warmblood and native breeds wore IMUs (Shimmer, 200Hz) to be led in a 25m straight line at walk and trot on asphalt, grass and sand (3 passes per gait). Stance durations (T) were calculated from hoof on and off obtained using both a reference (hoof) and novel (pastern) method. Error was assessed in terms of mean difference between pastern (Tp) and hoof (Th) calculated stance durations (accuracy) and SD of these (precision), as a percentage of total stride duration. Repeated measures ANOVA was used to compare errors under different conditions. Accuracy (Table 1) remained below 1% of a stride duration for most cases. Performance of the method was consistent on all surfaces, with no significant differences in most cases ( P = 0.4 fore and P = 0.08 hindlimb walk; P = 0.3 hindlimb trot). For forelimbs at trot differences between grass and sand compared with asphalt were significant ( P = 0.004 and P < 0.0001) but the magnitude of these, 0.5 and 0%, were negligible. To conclude, the novel method effectively detected gait events on 3 common surfaces for the fore and hindlimbs at walk and trot across multiple breeds, with high accuracy and precision on all surfaces. This will benefit future studies under field conditions, allowing reliable event detection on different surfaces.