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Recognition of pain in horses and other animals is important, because
pain is a manifestation of disease and decreases animal
welfare. Pain diagnostics for humans typically includes
self-evaluation and location of the pain with the help of
standardized forms, and labeling of the pain by an clinical expert
using pain scales. However, animals cannot verbalize their pain as
humans can, and the use of standardized pain scales is challenged by
the fact that animals as horses and cattle, being prey animals,
display subtle and less obvious pain behavior - it is simply
beneficial for a prey animal to appear healthy, in order lower the
interest from predators. The aim of this project is to develop a
method for automatic recognition of pain in horses. The method
employs an RGB-D sensor mounted in the stable ceiling, and detects
and recognizes behavioral patterns related to pain, in an automated
manner when the horse perceives itself as being alone. The Machine
Learning-based pain classification system is trained with examples
of behavioral traits labeled with pain level and pain
characteristics. This automated, user independent system for
recognition of pain behavior in horses will be the first of its kind
in the world. A successful system might change the concept for how
we monitor and care for our animals.
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