In the last years has been an increasing interest in developing tools to introduce concepts as smart-farming in agro-business. In this context, one problem revisited, with new technological tools, is the animal behavior analysis, because it has a profound impact on the core costs of dairy farms. Farming practices are facing higher increased scrutiny regarding cow welfare and health aspects and safety and welfare for farmworkers. In this sense, automated animal behavior monitoring systems have become a useful practice for research and animal production management purposes. Animal monitoring allows us to identify and to detect movements related to a specific pathology or animal behavior, for example, health state, estrus, lameness, or others. A significant difficulty in assessing this problem arises in identifying these movements in natural environments. The technology for automatic behavior detection uses MicroElectroMechanical (MEM) based Inertial Measurement Units (IMU) because they are cheap, portable, and sturdy. In the farming industry, there are devices developed for animal behavior detection, installed in ear-tags, collars, and even in leg-tag. The data processing for automatic detections relies on the accuracy of the IMU selected for the application these studies enable us to address problems like animal monitoring with new and enhanced tools in order to increase the coverage and to make these devices a productive tool to support dairy producers. However, all these developments use the information provided by the accelerometers without questioning the accuracy of the data provided. There is space to improve these detections with tools such as self-calibrating attitude estimation, among others.
Carlos Muñoz received the Electronic Engineer degree from Universidad de La Frontera, Temuco, Chile, in 1990, and the Ph.D. degree from Pontificia Universidad Catolica de Chile, Santiago Chile, in 2000, in Engineering Sciences and Industrial Automation. He joined the Universidad de La Frontera in Fall 1996, where he is currently an associate professor in the Department of Electrical Engineering. He has authored more than 35 publications, 20 of them are in Web of Science. His research interests include predictive control, stochastic control, wireless communications, and smart farms.