Leveraging Machine Learning to Analyze Canine Behavior and Neural Patterns: A Conceptual Framework for Non-Invasive Neural Health Monitoring

Companion animals, especially canines, enjoy many relationships with their owners; however, their health and well-being determine in large part the quality of these relationships. But the existing strategies of monitoring the health of dogs are concerned only with diagnosing visible signs, meaning that a lot is unknown with regard to dogs’ mental and emotional health. This paper presents a proposition of a conceptual model on how artificial intelligence (AI) and behavioral data could be used to assess the brain and ancillary emotions of dogs. The framework is based on the use of machine-learning analysis of simultaneous motion, vocalization, and heart rate patterns as synaptic proxies. The goal of this work is to train supervised algorithms with imitative behavioral datasets and investigate the link between some behavioral patterns and the risk of certain neural alterations like stress disorders, anxiety, cognitive degeneration, etc. Although the outcome will be modeled, the results will prepare the ground as regards the application of the approach with practical purposes directed towards the development of a non-invasive system for pets to monitor their brain health. This paper gives advocates the possibility of AI-based approaches for revolutionizing health care services for dogs, in which early detection and management will be possible in an advanced way. The proposed framework is a basis for the future development of pet neuroscience, veterinary medicine, and digital technology.