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Horse Automated Behaviour Identification Tool (2016)

The Horse Automated Behaviour Identification Tool (HABIT) is a interdisciplinary animal-computer interaction research project which could help us understand what animals are thinking and feeling. The aim of the software is to identify horse behaviour from unconstrained (amateur) video so we humans can interpret those reactions and understand why they are happening.

By bringing together experts in animal computer interaction, equitation science, ethology, animal behaviour and biomedical engineering the aim of HABIT is to develop a software programme that will automatically identify the behaviour horses are exhibiting and tell us whether the horse is stressed, sick or suffering.


In collaboration with Nottingham Trent University and The Open University.


Steve North. 2016. Do Androids dream of electric steeds?: the allure of horse-computer interaction. interactions 23, 2 (February 2016), 50-53. https://doi.org/10.1145/2882529

Carol Hall and Amanda Roshier. 2016. Getting the measure of behavior … is seeing believing?. interactions 23, 4 (June 2016), 42-46. https://doi.org/10.1145/2944164

Steve North and Clara Mancini. 2016. Introduction. interactions 23, 4 (June 2016), 34-36. https://doi.org/10.1145/2946043

Steve North, Carol Hall, Amanda Roshier and Clara Mancini. 2015. Habit (Horse Automated Behaviour Identification Tool) Video Poster. In The Second International Congress on Animal Computer Interaction (ACI2015). Proceedings of the 2015 International Workshops on Advances in Computer Entertainment Conference (ACE2015).  https://doi.org/10.13140/RG.2.1.4924.6480

Steve North, Carol Hall, Amanda Roshier and Clara Mancini. 2015. Habit: Horse Automated Behaviour Identification Tool – a Position Paper. In Proceedings of ACI@BHCI (Animal Computer Interaction Workshop), British HCI 2015. https://doi.org/10.13140/RG.2.1.3395.0881




Last updated: 13th April 2019

Mixed Reality Laboratory

University of Nottingham
School of Computer Science
Nottingham, NG8 1BB

telephone: +44 (0) 115 846 6780
email: mrl@cs.nott.ac.uk