Analysis of equine gaitprint and other gait characteristics using self-organizing maps (SOM)

Detection and evaluation of lameness by visual assessment requires the examiner to consider several different and rapidly changing body movement patterns. When the patterns become too complex, tools like artificial neural networks (ANN) can be useful. ANNs can be gainfully applied to gait analysis to distinguish stride characteristics and to identify pathological gait. The self-organizing map (SOM) is trained to cluster strain measurement data collected from a single hoof of moving horses. An analysis of the characteristics of the data and the effects of testing different data elements is examined. The method was successful in differentiating certain stride characteristics such as shoeing, gait, speed, and direction of movement and produced a unique model for each horse's gait.