Hiding in the Deep: Online Animal Activity Recognition using Motion Sensors and Machine Learning

The activity of animals is a rich source of information that not only provides insights into their life and well-being but also their environment. Animal activity recognition (AAR) is a new field of research that supports various goals, including the conservation of endangered species and the well-being of livestock. Over the last decades, the advent of small, lightweight, and low-power electronics has made it possible to attach unobtrusive sensors to animals that can measure a wide range of aspects such as location, temperature, and activity. These aspects are highly informative properties for numerous application domains, including wildlife monitoring, anti-poaching, and livestock management. In this thesis, we focus on AAR that aims to automatically recognize the activity from motion data – on the animal – while the activities are performed (online). Specifically, we use motion data recorded through an inertial measurement unit (IMU) that comprises an accelerometer, gyroscope, and magnetometer to classify up to eleven different activities.