An IoT-based Design Using Accelerometers in Animal Behavior Recognition Systems

Abstract

Cow behavior recognition systems support the assessment of cows’ condition by providing their behavior information. Accelerometers are particularly suited for a non-invasive solution for the development of these monitoring systems. They are cheap and simple in setting up and providing high-performance recognition when using machine learning algorithms. The activity complexity of animals brings challenges in real context applications because different behaviors may have similar acceleration data. The reason is that they contain similar gestures, for example, feeding and standing. In our previous work, we proposed a cows’ behavior classifier based on leg-mounted acceleration data. The distinguishing of similar behaviors, such as feeding and standing, is limited by the data. This study presents a new efficient cow behavior recognition system based on combining leg-mounted and collar-mounted accelerometers. Significantly, the acceleration data from these two sensors were synchronized. Therefore, we can substantially expand the amount of information for classification purposes. Our approach identifies four cow behaviors: walking, feeding, lying, and standing. Random Forest algorithm with our extracted features (root mean square, standard deviation, and mean) and 16-second data window (a sample/second) offer excellent performance when identifying all concerning behaviors: feeding (0.914 accuracy, 0.884 sensitivity, 0.956 positive predictive value), lying (0.998, 0.996, 1), standing (0.88, 0.928, 0.842), and walking (0.998, 0.996, 0.998). These performances are better than other existing works, especially in our experiments with free-grazing cows.