A full end-to-end analytical framework for livestock behavior modeling and health assessment using wearable electronic recording system and machine learning

• Proposed a novel end-to-end analytical framework for cattle behavior modeling and health assessment, integrating wearable electronic recording systems (WERS) with a machine learning-based intelligent analytical toolkit (IAT), covering the entire pipeline from data acquisition to decision support. • Supports customizable feature extraction and model optimization, with multiple options for data preprocessing, modeling, and analytical visualization, thereby enhancing both analytical accuracy and interpretability for precision livestock management. • Demonstrated high system efficacy through experimental validation, with behavior classification models achieving accuracies of 95.34% (neck-mounted sensor) and 90.18% (leg-mounted sensor), and the health assessment model reaching a peak accuracy of 92.93%. • Validated in real-world farm deployment, ensuring stable data collection, real-time edge analytics, and dynamic health assessment outputs, while providing intuitive visual feedback for farm personnel to support informed decision-making. Precision Livestock Farming (PLF) aims to enhance animal management through technology, yet its progression is limited by a disconnect between discrete data collection tools and the practical requirement for unified, interpretable decision-support systems. While wearable sensors and machine learning offer potential for behavior monitoring, current solutions are often fragmented, focusing on isolated classification tasks rather than providing a complete, actionable pipeline from raw data to farm management insights. This lack of integration, alongside the technical challenges of model optimization, significantly hinders widespread practical adoption. This work presents a full end-to-end analytical framework that integrates wearable electronic recording system (WERS) hardware with intelligent analytical toolkit (IAT) software to form a fully automated workflow. The IAT incorporates automated model selection and hyperparameter tuning across twelve machine learning algorithms, three feature extraction methods, and six feature selection strategies, enabling flexible and customizable modeling pipelines for sequential data processing, behavior recognition and health evaluation, and visual feedback. The implemented system demonstrates high classification accuracy, strong adaptability, and robust support for cattle behavior sequence analysis and health assessment. The system has been empirically validated on eight dairy cattle over a six-day period, demonstrating its practical applicability in real-world conditions based on the real-time deployment platform built for the system. By providing a systematic and scalable solution for intelligent livestock monitoring, this work bridges the gap between fragmented sensing technologies and operational decision-support systems, ultimately contributing to improved decision-making and operational efficiency in PLF management.