Intelligent Sensing and Application of Animal Behaviors Based on Wearable Sensors: A Review

Accurate, efficient, and timely monitoring of animal behaviors is of great significance to precision livestock farming (PLF). Behavioral monitoring serves as a valuable tool for daily production management for farmers. It can be utilized for assessing animal health, detecting estrus, monitoring parturition, and even estimating feed intake. Non-contact machine vision and contact sensors (such as accelerometers, pressure sensors and position sensors) embedded in wearable devices represent the forefront of current research in behavior monitoring. Due to the continuity and traceability in the contact sensing of wearable sensors, this study focused on contact sensing techniques and reviewed the characteristics of various sensing methods. It addressed challenges in data sorting, advancements in identification algorithms, potential industrial applications following behavior recognition, and the associated challenges and prospects. Current behavior classification algorithms primarily rely on traditional machine learning or deep learning approaches, which exhibit the limitations such as high-frequency data acquisition, complex processing requirements, and low adaptability to the intricate scenarios encountered by wearable devices. As a new frontier in machine learning that is adaptable to application scenarios with limited computational resources, the potential use of Tiny Machine Learning (Tiny ML) in behavior recognition was also analyzed to provide guidance for subsequent research and applications.