This study explores the application of wearable sensor technology for enhancing animal welfare in livestock through monitoring and analysis of behavioral patterns. Animal welfare is a critical aspect of livestock management, ensuring the well-being and health of animals. Traditional methods of assessing animal welfare rely on subjective observations and manual monitoring, which can be time-consuming and prone to human error. The utilization of wearable sensor technology offers a novel approach to monitor animal behavior in real-time, enabling early detection of potential welfare issues. This research investigates various types of wearable sensors, such as accelerometers and GPS trackers, and their ability to collect data on behavioral patterns in livestock. The collected data is analyzed using machine learning algorithms to identify abnormal behaviors and indicators of stress or discomfort. The findings of this study demonstrate the potential of wearable sensor technology in improving animal welfare by facilitating proactive management and targeted interventions.
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