The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 5955
• Main Authors: Redmond, Cushla, Smit, Michelle, Draganova, Ina, Corner-Thomas, Rene, Thomas, David, Andrews, Christopher
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.09.2024; MDPI
• Subjects: Accelerometers; Accelerometry - instrumentation; Accelerometry - methods; algorithm; Algorithms; Animals; Batteries; Behavior; Behavior, Animal - physiology; behaviour classification; Data collection; Data mining; Dogs; Female; Locomotion - physiology; Machine Learning; Male; Nutrition research; overall activity; random forest; Surveillance; New Zealand; United States > US; random forest; behaviour classification; overall activity; algorithm
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24185955

Assessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time consuming, labour-intensive, and subject to bias,...