Technical note: Evaluation of a triaxial accelerometer for monitoring selected behaviors in dairy calves

Abstract

The objectives of this study were (1) to develop an algorithm for the acceleration sensor of the Smartbow Eartag (Smartbow GmbH, Weibern, Austria) to distinguish between postures (lying and standing or locomotion) and to detect 6 kinds of activities (milk intake, water intake, solid feed intake, ruminating, licking or sucking without milk intake, and other activities) in dairy calves and (2) to evaluate this sensor for identifying these behaviors in dairy calves compared with observations from video. Accelerometers were applied to the left ears of 15 preweaned Holstein dairy calves. Calves were kept in a group pen and received milk replacer from an automatic calf feeder. Based on 38 h of acceleration data and video observation, an algorithm was established to detect the predefined behaviors. Using cross-validation, video recordings were used to analyze whether a behavior was detected correctly by the developed algorithm. For posture, sensitivity (94.4%), specificity (94.3%), precision (95.8%), and accuracy (94.3%) were high. Cohen's kappa was calculated as 0.88. For the 6 defined activities, overall (i.e., aggregated for all activities) accuracy was 70.8% and kappa was calculated as 0.58. Some activities (e.g., ruminating, feed intake, other activities) were identified better than others. In conclusion, the developed algorithm based on the acceleration data of the Smartbow Eartag was successful in detecting lying behavior, rumination, feed intake, and other activities in calves, but further development of the underlying algorithm will be necessary to produce reliable results for milk and water intake.

Symposium review: Precision technologies for dairy calves and management applications

“…For example, one ear-tag accelerometer for herd management was validated for rumination against live observation in one study (Hill et al, 2017), but was not validated for rumination or feeding time against video observations in another study (Reynolds et al, 2019). A different in-ear herdmanagement accelerometer also had moderate agreement for rumination but poor agreement for feeding behaviors such as milk intake from a teat, water, and solid feed intake (Roland et al, 2018a) and milk intake from a bucket (Roland et al, 2018b). Moreover, a halter-based research accelerometer was validated in calves for measuring rumination, provided that events less than 5 min in duration were removed (Eslamizad et al, 2018).…”

Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal’s Left Flank

“…Statistical analyses were carried out using R computing environment, version 3.2.1 (R Core Team 2013, R Foundation for Statistical Computing, Vienna, Austria). The input dataset had for each record the tri-axial accelerations, which were collected every 0.2 s. The total number of observations from the 12 cows was 490,900, which is equal to 27.3 h, in line with other similar experiments [15,25]. The observations that the researchers were not able to assign to a univocal behavior or posture were removed from the dataset.…”

“…Accelerometers have shown to be effective in detecting behaviors of cattle, such as ruminating, feeding, lying, or walking [15][16][17], depending on the site where the sensor is fixed to the animal. Although in previous studies a satisfactory detection of few behaviors was reached, the prediction performance tended to drop when both posture and behaviors (feeding, ruminating, walking, resting, etc.)…”

Discriminating spontaneous locomotor play of dairy calves using accelerometers

“…Moreover, acceleration measurements have been used to quantify lying and standing when studying effects of social housing on weaning (Overvest et al, 2018) and determining effects of different disbudding methods on lying behavior (Sutherland et al, 2018a). Although the accuracy of recording general activities from accelerometers is high, the validation of recording specific behaviors such as feeding and ruminating is still in progress (e.g., Roland et al, 2018).…”