Deep Learning-based Animal Activity Recognition with Wearable Sensors : Overview, Challenges, and Future Directions

Animal behavior, as one of the most crucial indicators of animal health and welfare, provides rich insights into animal physical and mental states. Automated animal activity recognition (AAR) allows caretakers to monitor animal behavioral variations in real time, significantly reducing workloads and costs in veterinary clinics and promoting livestock management efficiency. With recent advances in sensing technologies and smart computing techniques, automated AAR has been increasingly studied, and tremendous successes have been achieved. This paper provides a comprehensive summary of recent research on AAR based on wearable sensors and deep learning algorithms. First, the commonly used sensor types and frequently studied animal species and activities are described. Then, an extensive overview of deep learning-based methods for wearable sensor-aided AAR is presented, according to the taxonomy of deep learning algorithms. We also provide a comprehensive list of publicly available datasets collected via wearable sensor-aided AAR over the past five years. This list can serve as a valuable resource for readers who wish to further explore the field of AAR. In addition, we discuss potential challenges associated with the development of deep learning models for AAR and suggest potential solutions and future research directions for these challenges. In conclusion, this review work provides rich inspiration for the future advancement of robust AAR systems based on wearable sensors and deep learning techniques. When combined with qualitative assessments of veterinary specialists, the accurate and quantitative results obtained by automated AAR systems hold the potential to significantly improve animal health and welfare. © 2023 Elsevier B.V. All rights reserved.