{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:18:59Z","timestamp":1787876339436,"version":"build-2784847793"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>This paper describes an emotion recognition system for dogs automatically identifying the emotions anger, fear, happiness, and relaxation. It is based on a previously trained machine learning model, which uses automatic pose estimation to differentiate emotional states of canines. Towards that goal, we have compiled a picture library with full body dog pictures featuring 400 images with 100 samples each for the states \u201cAnger\u201d, \u201cFear\u201d, \u201cHappiness\u201d and \u201cRelaxation\u201d. A new dog keypoint detection model was built using the framework DeepLabCut for animal keypoint detector training. The newly trained detector learned from a total of 13,809 annotated dog images and possesses the capability to estimate the coordinates of 24 different dog body part keypoints. Our application is able to determine a dog\u2019s emotional state visually with an accuracy between 60% and 70%, exceeding human capability to recognize dog emotions.<\/jats:p>","DOI":"10.3390\/fi14040097","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T14:55:35Z","timestamp":1647960935000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut"],"prefix":"10.3390","volume":"14","author":[{"given":"Kim","family":"Ferres","sequence":"first","affiliation":[{"name":"Department of Information Systems and Information Management, University of Cologne, Pohligstrasse 1, 50969 Cologne, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4765-2287","authenticated-orcid":false,"given":"Timo","family":"Schloesser","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Information Management, University of Cologne, Pohligstrasse 1, 50969 Cologne, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7271-3224","authenticated-orcid":false,"given":"Peter A.","family":"Gloor","sequence":"additional","affiliation":[{"name":"MIT Center for Collective Intelligence, 245 First Street, Cambridge, MA 02142, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,22]]},"reference":[{"key":"ref_1","unstructured":"Snyder, L.M., and Moore, E.A. 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