Abstract
In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.
Highlights
Compiling evidence that non-human animals are able to use complex cognitive process and show emotions is appearing
More studies keep appearing that show a correlation between indirect measures and the results of such behavioural tests, and such measures provide a more efficient method of monitoring animal affective state without the need for individual behavioural testing
Research on humans have resulted in complex systems that allow accurate sentiment analysis, preference detection [5] using qualitative and quantitative data such as facial expressions, body gestures, phonetic and acoustic properties of spoken language, word use and grammar in written text and more [8, 68]
Summary
Compiling evidence that non-human animals are able to use complex cognitive process and show emotions is appearing. More studies keep appearing that show a correlation between indirect measures and the results of such behavioural tests, and such measures provide a more efficient method of monitoring animal affective state without the need for individual behavioural testing Such indirect measures include hormonal assessments, physiological measures, facial expression, brain activity, thermal imaging, vocalisations and movement [30]. The measure that is used as an indicator of affective state will be [1] non-invasive, [2] will produce high-quality data with low sensitivity to environmental disturbances, [3] can be automated to reduce the time and labour required to collect the data, [4] is able to track affective states of individual animals, [5] is not too costly and [6] can identify subtle changes to allow prevention of negative affective state.
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