Transformer-Based Dog Behavior Classification With Motion Sensors

作     者：Or, Barak 

作者机构：MetaOr Artificial Intelligence CEO Off IL-3349602 Haifa Israel Reichman Univ Google Reichman Tech Sch IL-4610101 Herzliyya Israel 

出 版 物：《IEEE SENSORS JOURNAL》 (IEEE Sensors J.)

年 卷 期：2024年第24卷第20期

页      面：33816-33825页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0702[理学-物理学] 

主　　题：Dogs Transformers Motion detection Sensors Computational modeling Data models Computer architecture Accelerometer attention mechanism deep neural network (DNN) dog activity detection dog behavior gyroscope inertial sensors long short-term memory (LSTM) machine learning mode recognition motion sensors pet activity detection (PAD) real-time supervised learning transformers 

摘      要：This article 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.