Potential of accelerometers to remotely early detect bovine ephemeral fever in cattle using pattern mining.

Abstract

Bovine Ephemeral Fever (BEF), caused by an arthropod-borne rhabdovirus, is widespread in tropical and subtropical regions. It affects cattle with symptoms of fever, lameness, inappetence and in some situations can result in mortality. The goal of this study is to determine if accelerometer data can be used to identify the behavior patterns that occur when cattle become ill from BEF. Eight heifers in a separate experiment were monitored with 3-axis accelerometers sensors. Movement variation (MV) was calculated from accelerometer data (25 Hz) using 1-min epochs and then averaged hourly. Two different approaches, cosine similarity (CS) and deviation from previous behavioral patterns, were developed to autonomously detect patterns and recognize the onset of sickness in cattle using accelerometer data. Analyses show that one heifer had behavioral changes one day before the manager observed BEF, and another heifer had behavioral changes on the same day the manager observed BEF. The other six heifers did not display any BEF symptoms. To validate the efficacy of our analytical approaches, we employed them on a separate commercial herd of 73 cows where 4 of the 27 monitored cows were observed with BEF symptoms. Predictions were either on the day or even the day prior to the manager's observation and diagnosis. There were likely no false positives in the first or second trials using the deviation algorithm with $sum\_deviation$ formula, but there were several false positives with the other algorithms. These case studies demonstrate the potential of accelerometer data to autonomously detect disease onset, in some cases before it was apparent to the human observer. However, more research is needed to minimize false positives that may occur from other similar diseases, abnormal weather events or cyclical changes in behavior such as estrus is required.