Smart wearable biosensors for real-time bovine health monitoring and early disease detection

Thesis Abstract

The study addresses the critical need for continuous, objective bovine health monitoring to enable timely intervention for infectious and metabolic diseases that incur substantial economic losses in modern cattle production. Despite advances in precision livestock farming, real-time automated detection of health deterioration remains limited by sensor integration, data analytics, and interpretability for on-farm decision-making. The aim is to develop and evaluate a smart wearable biosensor platform that integrates physiological, behavioral, and biochemical signals to enable real-time health status assessment and early disease detection in dairy cattle. Specific objectives are (1) to design and validate a multi-sensor collar that concurrently measures body temperature, activity patterns, rumination, heart rate variability, locomotion, and salivary or sweat-based biomarkers indicative of inflammatory and metabolic states; (2) to develop edge-computing algorithms for anomaly detection and health scoring using supervised learning and time-series analysis; (3) to establish a robust data fusion framework linking sensor outputs with veterinarian-confirmed disease events; (4) to evaluate the platform’s diagnostic performance against standard veterinary clinical records, laboratory tests, and farm production data; and (5) to assess on-farm feasibility including user acceptance, data security, and economic viability. The methodology adopts a mixed-methods, longitudinal design conducted on a selected cohort of 200 lactating Holstein cows across two commercial dairy herds over 18 months. The population comprises adult cows with electronic identification and access to automated milking and housing systems. A stratified sampling approach yields subgroups representing healthy controls (n=60) and cows subsequently diagnosed with common diseases (n=140), such as subclinical mastitis, ketosis, displaced abomasum, and metritis. The data collection instruments include a multi-parameter wearable collar with integrated sensors (triaxial accelerometer, infrared thermistor for skin temperature, photoplethysmography for heart rate, galvanic skin response proxy for stress), a non-invasive saliva/sweat biosensor module for inflammatory markers (CRP proxy, lactate, cortisol), and a wireless data gateway transmitting to a cloud-based analytics platform. Veterinary clinical records, weekly herd health reports, milk yield and composition data, and farm management logs constitute supplementary data sources. Validity and reliability of instruments are established through calibration experiments, test-retest assessments, and cross-validation against laboratory assays (ELISA for CRP equivalents, lactate dehydrogenase assays). Data analysis proceeds in three strands (i) time-series analysis and machine learning, including LSTM neural networks and random forest classifiers, to generate a dynamic HealthIndex and early-warning alerts; (ii) multivariable regression and survival analyses to quantify associations between sensor-derived features and disease onset; and (iii) economic evaluation using activity-based costing and net present value of early-detection interventions. A thematic analysis of farmer feedback is conducted to assess usability and acceptability, ensuring alignment with Theory of Planned Behavior to interpret intended and actual usage patterns. Expected findings include high diagnostic accuracy of the integrated biosensor platform, with area under the receiver operating characteristic curve (AUC) exceeding 0.88 for early disease detection up to 5–7 days before clinical diagnosis, and a HealthIndex that correlates strongly with veterinary-confirmed health events (? > 0.60, p < 0.01). Sensitivity analyses are anticipated to demonstrate robustness across herd environments and disease categories. The study contributes to knowledge by demonstrating the feasibility and added value of multimodal wearable biosensors, bridging physiological signals, behavioral metrics, and biochemical biomarkers within an edge-computing framework to deliver timely, farm-scale health insights and decision support. It advances a data-driven model for proactive herd health management that can be generalized to different breeds and production systems, contingent on calibration to local baselines. The conclusion is that integrated wearable biosensors can substantially improve early disease detection and animal welfare, while reducing treatment costs and production losses. Recommendations include deployment best practices for sensor maintenance and calibration, data governance and privacy protocols, farmer training modules, and pathways for scalable adoption, along with future work to incorporate genomic and microbiome data for enhanced predictive capability.