RT Journal Article
T1 Identifying livestock behavior patterns based on accelerometer dataset
A1 Rodríguez Baena, Domingo Savio
A1 Gómez-Vela, Francisco Antonio
A1 García Torres, Miguel
A1 Divina, Federico
A1 Barranco, Carlos D.
A1 Díaz-Díaz, Norberto
A1 Jiménez, Manuel
A1 Montalvo, Gema
K1 Time series processing
K1 Livestock activity
K1 Pattern recognition
AB In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However, in large breeding farms this may not represent the best approach, since, in this way, not all the animals can be effectively monitored all the time. Moreover, the traditional approach heavily rely on human experience, which cannot be always taken for granted. To this aim, in this paper, we propose a new method for automatically detecting activity and inactivity time periods of animals, as a behavior indicator of livestock. In order to do this, we collected data with sensors located in the body of the animals to be analyzed.In particular, the reliability of the method was tested with data collected on Iberian pigs and calves. Results confirm that the proposed method can help breeders in detecting activity and inactivity periods for large livestock farming.
PB Elsevier
YR 2020
FD 2020
LK https://hdl.handle.net/10433/19664
UL https://hdl.handle.net/10433/19664
LA en
NO Journal of Computational Science, vol. 41, p. 101076
NO Deporte e Informática
DS RIO
RD May 22, 2026