Transformer-based Dog Behavior Classification with Motion Sensors

This paper deals with classifying dog behavior using motion sensors, leveraging a transformer-based Deep Neural Network (DNN) model. Understanding dog behavior is essential for fostering positive relationships between dogs and humans and ensuring their well-being. Traditional methods often fall short in capturing temporal dependencies and efficiently processing high-dimensional sensor data. Our proposed architecture, inspired by its success in Natural Language Processing (NLP), utilizes the self-attention mechanism of the transformer to effectively identify relevant features across various time scales, making it ideal for real-time applications. The architecture includes only the encoder part with a classifier's head to output probabilities of dog behavior. We used an open-access dataset focusing on seven different dog behavior, captured by motion sensors on top of the dog's back. Through experimentation and optimization, our model demonstrates superior performance with an impressive accuracy rate of 98.5%, outperforming time-series DNN models. The model's efficiency is further highlighted by its reduced computational complexity, lower latency, and smaller size, making it well-suited for deployment in resource-constrained environments.