Scientific approach

We complement traditional veterinary routines, by using horse's behavior as primary symptom source. We use big data and deep learning methods on robust behavior data. Our collaboration with veterinarians and researchers ensures a development aligned with expertise and field experience from the veterinary profession. By this we are able to do earlier identification of health issues than has been possible with current methodology. At the same time provide instant indicators of potential injures, health issues or mental hazards. 

Objective behavioral analysis

Rich behavioral data

HoofSteps sensor is placed on the forehead of the horse. This location gives the most complete data on behaviors, such as chewing. Drinking and eating behavior is an important indicator of a horse's well-being or pain experience. 

Growing behavioral data set

All movements derived from the head position combined with the position of the horse gives all data required for our algorithms, thus telling the horses current behavior. 


By combining different behavior we receive information that could be an indicator of an evolving health issue, such as colic or laminitis. 


Our model emanates from a numerous of basic behaviors that build a profile for each individual horse over time as well as a common general behavioral profile. By using our solution in a group of horses we identify social behavior and interaction.


Other diverges from an individual pattern, combined with back ground data of the horse, could be an indicator of situation of importance, such as approaching foaling, stress or uneven motion pattern.

If you are a researcher interested in using our data donĀ“t hesitate to contact us.