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STABLE - Smart Behaviour Learning for Horses

Reference number
Coordinator Högskolan i Halmstad - Center for Applied Intelligent Systems Research (CAISR)
Funding from Vinnova SEK 126 665
Project duration November 2018 - September 2019
Status Completed
Venture Personal mobility between societal sectors
Call Funding for staff exchange and artificial intelligence (AI)

Purpose and goal

The goal was to be able to model horse behaviour and to predict anomalies. Goal was reached by using the GRAND method to model movement in similar time periods in the past. Anomalies are detected by comparing current movement to historic movement. The GRAND algorithm calculates a deviation score which can be used to generate an alarm if it falls outside the user-defined boundaries.

Expected results and effects

We expected the following results 1) A way to quantify the level of normal behaviour for a horse, and device a method to detect deviations from the normal level. This goal has been reached. 2) Provide a working prototype. The system is being incorporated into the Vidiquus system at the moment. 3) A paper describing the system and results. This is being worked on, but is challenging because it is hard to evaluate the found anomalies without expert knowledge.

Planned approach and implementation

The plan was to model horse behaviour using webcam images, and use an anomaly detection algorithm to detect deviations from normal behaviour. Movement data provided by Videquus AB was used to train and test the deviation detection algorithm. The movement data was generated from the images from the webcams monitoring the horses in their stables.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 27 October 2018

Reference number 2018-04356

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