Discrimination of healthy versus sick steers by means of continuous remote monitoring of animal activity.

Objective: To test a unique electronic ear tag designed to collect movement data to determine whether physical activity of sick steers differed from that of healthy steers.
Animals: 206 steers.
Procedures: Physical activity in 2 groups of steers during November and December of 2010 (101 steers; the tag of 1 steer failed, and thus that steer was removed from the study, which resulted in data for 100 steers) and 2011 (105 steers) was monitored with an electronic ear tag device with an on-board triple-axis accelerometer. The accelerometer recorded motion in all 3 axes in the form of counts per minute. A radio-frequency transmitter on the ear tag delivered serial packets of motion data to a local server. An algorithm was developed to analyze the activity data to determine whether this technique could be used to assess health status with high accuracy.
Results: Steers that became sick had significantly fewer activity counts (approx 25% fewer), compared with the activity counts of steers that remained healthy the entire time.
Conclusions and Clinical Relevance: In this study, automated detection of health status in growing cattle was feasible through remote monitoring of animal activity. Early identification of sick animals should lead to improved health outcomes, increased marketability, and improved animal well-being and help to minimize the use of antimicrobials that could contribute to resistant bacteria.