Comparison of a body-mounted inertial sensor system–based method with subjective evaluation for detection of lameness in horses

Objective—To compare data obtained with an inertial sensor system with results of subjective lameness examinations performed by 3 experienced equine veterinarians for evaluation of lameness in horses. Animals—106 horses. Procedures—Horses were evaluated for lameness with a body-mounted inertial sensor system during trotting in a straight line and via subjective evaluation by 3 experienced equine practitioners who performed complete lameness examinations including lunging in a circle and limb flexion tests. Agreement among evaluators regarding results of subjective evaluations and correlations and agreements between various inertial sensor measures and results of subjective lameness evaluations were determined via calculation of Fleiss’ k statistic, regression analysis, and calculation of 95% prediction intervals. Results—Evaluators agreed on classification of horses into 3 mutually exclusive lameness categories (right limb lameness severity greater than left limb lameness severity, left limb lameness severity greater than right limb lameness severity, or equal right and left limb lameness severity) for 58.8% (k = 0.37) and 54.7% (k = 0.31) of horses for forelimb and hind limb lameness, respectively. All inertial sensor measures for forelimb and hind limb lameness were positively and significantly correlated with results of subjective evaluations. Agreement between inertial sensors measures and results of subjective evaluations was fair to moderate for forelimb lameness and slight to fair for hind limb lameness. Conclusions and Clinical Relevance—Results of lameness evaluation of horses with an inertial sensor system and via subjective lameness examinations were significantly correlated but did not have strong agreement. Inertial sensor-based evaluation may augment but not replace subjective lameness examination of horses.

Kevin G. Keegan, David A. Wilson, Joanne Kramer, Shannon K. Reed, Yoshiharu Yonezawa, Hiromitchi Maki, P. Frank Pai, Marco A. F. Lopes