Lameness detection in sheep through behavioural sensor data analysis

Lameness is a clinical symptom referring to locomotion changes, resulting in impaired and erratic movements that differ widely from normal gait or posture. Lameness has an adverse impact on both sheep welfare and farm economy, therefore the preclinical detection of lameness will improve both sheep health and, in turn, support farming businesses. A newly developed sensor technology should enable automatic monitoring of animals to determine physiological and behavioural indicators, which would then be subsequently used as inputs into data analysis algorithms. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides acceleration, angular velocity, orientation, longitude, latitude and the time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to detect lameness in sheep by analysing the data retrieved from a mounted sensor on the neck of the sheep. This model will help the shepherd to detect lame sheep earlier, to prevent trimming or even culling.