Dog Activity Detection and Recognition

This paper presents a comprehensive study on a sensor-based dog activity recognition system aimed at enhancing canine well-being. Utilizing wearable sensors and machine learning algorithms, the research explores methodologies for data collection, annotation, preprocessing, and model training. The system demonstrates a promising accuracy rate of 79% in identifying activities such as lying, sitting, walking, and running, with particular success in recognizing stationary behaviors. Challenges in hardware selection and the practical implementation of wearable technology are addressed, highlighting the system's potential for real-time monitoring and scalable solutions. Future work focuses on expanding the system's applicability to a wider range of dogs and developing mobile applications for user-friendly monitoring. The study contributes to advancing pet care technology, offering pet owners reliable tools for tracking their dogs' behaviors and promoting animal welfare through technological innovations.