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Abstract
Despite the wide range of uses of rabbits (Oryctolagus cuniculus) as experimental models for pain, as well as their increasing popularity as pets, pain assessment in rabbits is understudied. This study is the first to address automated detection of acute postoperative pain in rabbits. Using a dataset of video footage of n = 28 rabbits before (no pain) and after surgery (pain), we present an AI model for pain recognition using both the facial area and the body posture and reaching accuracy of above 87%. We apply a combination of 1 sec interval sampling with the Grayscale Short-Term stacking (GrayST) to incorporate temporal information for video classification at frame level and a frame selection technique to better exploit the availability of video data.
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Details
1 University of Haifa, Information Systems Department, Haifa, Israel (GRID:grid.18098.38) (ISNI:0000 0004 1937 0562)
2 Israel Institute of Technology, Faculty of Electrical Engineering, Technion, Haifa, Israel (GRID:grid.6451.6) (ISNI:0000 0001 2110 2151)
3 University of Calgary, Faculty of Veterinary Medicine, Calgary, Canada (GRID:grid.22072.35) (ISNI:0000 0004 1936 7697)
4 São Paulo State University (UNESP), School of Veterinary Medicine and Animal Science, São Paulo, Brazil (GRID:grid.410543.7) (ISNI:0000 0001 2188 478X)