Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle

• Published in: Computers and electronics in agriculture 2009-06, Vol.67 (1), p.80-84
• Main Authors: Robert, B., White, B.J., Renter, D.G., Larson, R.L.
• Format: Article
• Language: English
• Subjects: accuracy; Activity; Agronomy. Soil science and plant productions; animal behavior; Behavior; Biological and medical sciences; Cattle; data analysis; data collection; Decision classification; Fundamental and applied biological sciences. Psychology; locomotion; measurement; measuring devices; prediction; remote sensing; temporal variation; three-dimensional accelerometers; Tri-axial accelerometry; veal calves
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2009.03.002

Cattle behavior is potentially a valuable indicator of health and well-being; however, natural movement patterns can be influenced by the presence of a human observer. A remote system could augment the ability of researchers, and eventually cattle producers, to monitor changes in cattle behavior. Constant video surveillance allows non-invasive behavior monitoring, but logging the movement patterns on individual animals over long periods of time is often cost prohibitive and labor intensive. Accelerometers record three-dimensional movement and could potentially be used to remotely monitor cattle behavior. These devices collect data based on pre-defined recording intervals, called epochs. Our objectives were to (1) determine if accelerometers can accurately document cattle behavior and (2) identify differences in classification accuracy among accelerometer epoch settings. Video-recorded observations and accelerometer data were collected from 15 crossbred beef calves and used to generate classification trees that predict behavior based on accelerometer data. Postural orientations were classified as lying or standing, while dynamic activities were classified as walking or a transition between activities. Video analysis was treated as the gold standard and logistic regression models were used to determine classification accuracy related to each activity and epoch setting. Classification of lying and standing activities by accelerometer illustrated excellent agreement with video (99.2% and 98.0% respectively); while walking classification accuracy was significantly ( P < 0.01) lower (67.8%). Classification agreement was higher in the 3 s (98.1%) and 5 s (97.7%) epochs compared to the 10 s (85.4%) epoch. Overall, we found the accelerometers provided an accurate, remote measure of cattle behavior over the trial period, but that classification accuracy was affected by the specific behavior monitored and the reporting interval (epoch).