Artificial intelligence and IoT-based biomedical sensors for intelligent cattle husbandry systems

Abstract

The presence of many animals with different body types and characteristics necessitates the need for cattle husbandry system. Especially in cows, elevated heart rates have been linked to symptoms of stress, such as sweating and anguish, and they can provide valuable information for monitoring systems. There are many ways to measure a cow’s pulse rate. In the current study, cow’s pulmonary function, cardiovascular system, contemplation rates and durations have been measured using biomedical sensors. An electro cardiogram-based sensor (ECS) approach for noninvasively monitoring the ruminant’s ingestive activity has also been developed in this study. A sensor adapter is used to sample the chewing surface Electrocardiogram (ECG) signal from ruminant animals’ masseter muscles while eating. When it comes to this, an intra-ruminal real-time sensor is designed to get accurate information on the ruminal activity of cows while grazing. A Fixed-Length Feature Extraction (FLFE) algorithm is used to analyze the respiratory rate and other factors. Four segmentation methods that have been tested and used to split the chewing signal automatically are Blind Fragmentation (BF), Fixed Duration Peak-Centered Segmentation (FDPCS), Double Onset Segmentation (DOS) and Fixed-Length Feature Extraction (FLFE) algorithm. Digital components have been integrated into an IoT-enabled digital platform for commercial use. The obtained ECG signal is extracted and segmented to analyze the respiratory rate and split the signal chewing state. Its main goal is to automate some monotonous animal care activities using Internet of Things and Artificial Intelligence (AI) to ensure better care and management of animals. The FLFE algorithm yielded a prediction accuracy of 94.5% for the respiratory rate of the cow when compared to healthy animals.