Ground reaction forces in horses, assessed from hoof wall deformation using artificial neural networks

An artificial neural network (ANN) was developed to investigate whether hoof wall deformation could be used to determine ground reaction forces (GRF) in horses. The ANN was taught this relationship under certain conditions and was able to generalise this knowledge to conditions for which it was not trained before. To acquire data to train and test the ANN, a horse was equipped with strain gauges attached to the dorsal, lateral and medial parts of the hoof to assess hoof wall deformation. A force plate was used to measure the GRFs. Both hoof wall deformation and GRF were recorded simultaneously at different speeds, gaits, surfaces and loads. An ANN was trained with some of these data, and subsequently provided with strain gauge recordings of strides, not used for training. By comparing the GRF calculated by the ANN based on the hoof wall deformation with that recorded simultaneously by the force plate, the generalisability of the ANN was determined. It was found that an ANN is capable of 'learning' the relationship between hoof wall deformation and GRF, and to generalise it to a wide range of new conditions. This technique enables assessment of GRF under difficult conditions, such as on a treadmill or on surfaces where a force plate cannot be employed.