Ground reaction forces in horses, assessed from hoof wall deformation using artificial neural networks.
Abstract: 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.
Publication Date: 1997-05-01 PubMed ID: 9354277DOI: 10.1111/j.2042-3306.1997.tb05041.xGoogle Scholar: Lookup
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- Journal Article
Summary
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This research focused on developing an artificial neural network (ANN) that could determine ground reaction forces (GRF) in horses based on the deformation of the hoof wall. The successful development of this network could enable the assessment of GRF in conditions where traditional methods are unfeasible.
Artificial Neural Network Development
- An artificial neural network was developed with the intent to deduce the relationship between hoof wall deformation and the GRF in horses.
- The ANN was trained under specific conditions to understand this relationship.
- The ability of the ANN to generalize this knowledge to conditions it was not trained for was tested.
Data Collection
- To gather the necessary data to train the ANN, strain gauges were attached to the dorsal, lateral, and medial parts of a horse’s hoof. These gauges measured the deformation of the hoof wall.
- A force plate was used to assess the GRFs at the same time that the strain gauges measured the deformation of the hoof wall. This was done at varying speeds, loads, surfaces, and gates.
Testing and Generalising the ANN
- The ANN was trained using some of the collected data, with the remaining data used for testing.
- The ANN was given strain gauge recordings of strides not previously used in the training phase.
- The GRF determined by the ANN based on the hoof wall deformation was compared to the GRF recorded simultaneously by the force plate.
- This comparison was used to determine the generalizability of the ANN under new condition.
Findings of the Study
- The results demonstrated that the ANN was capable of ‘learning’ the relationship between the hoof wall deformation and the GRF.
- Furthermore, the ANN was able to accurately generalize this knowledge to a wide range of previously unencountered conditions.
- Successfully developing and implementing this technique allows for the assessment of GRF under difficult conditions like a treadmill or surfaces where a force plate cannot be used.
Cite This Article
APA
Savelberg HH, Van Loon T, Schamhardt HC.
(1997).
Ground reaction forces in horses, assessed from hoof wall deformation using artificial neural networks.
Equine Vet J Suppl(23), 6-8.
https://doi.org/10.1111/j.2042-3306.1997.tb05041.x Publication
Researcher Affiliations
- Equine Biomechanics Research Group, Faculty of Veterinary Medicine, Utrecht University, The Netherlands.
MeSH Terms
- Animals
- Avena / physiology
- Exercise Test / methods
- Exercise Test / veterinary
- Hoof and Claw / physiology
- Horses / physiology
- Locomotion / physiology
- Male
- Neural Networks, Computer
- Physical Conditioning, Animal / physiology
- Walking / physiology
- Weight-Bearing / physiology
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