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News Article

Study identifies novel behavioural differences between lame and non-lame sheep


Behavioural differences between lame and non-lame sheep could be used to develop an automated system for lameness detection

Researchers from the School of Veterinary Medicine and Science at the University of Nottingham have been able to demonstrate the automated detection of lameness in sheep when standing, lying and walking, using a new prototype tagging and monitoring system. Their findings are published in Royal Society Open Science.

The technology was developed by Dr Jasmeet Kaler, Associate Professor in Epidemiology and Farm Animal Health from the University, along with the company Intel and agricultural software developer Farm Wizard.

The smart wearable technology consists of a sensing device worn on a sheep’s ear tag that gathers accelerometer and gyroscope data, effectively tracking the animal’s behaviour and movement and its way of walking. The algorithms are used to create different alerts for farmers.

For all three activities (standing, walking and lying), the study identified features that differed between lame and non-lame sheep. This is particularly novel in lying and standing, which have unobvious lameness related behaviours that had been difficult to spot with the human eye previously.

The results suggest that instead of affecting how much of an activity lame sheep do, it shows that they actually carry out activities differently, leading to a change in acceleration and rotational movement.

Detecting features that significantly differentiate lame from non-lame was not surprising because of visual differences previously reported between the gait pattern of lame and non-lame sheep. Five out of the top six characteristics when walking were frequency-domain features, linked to rhythm and pace. These differences could be linked to reduced mobility because of the disease in lame sheep, which also resulted in differences in the regularity and frequency of head movements.

Lame sheep also showed a change in gait with peculiar head nodding in line with stride compared to non-lame sheep which had a smoother stride pattern.

An interesting find was that the results for classification of lameness had a higher accuracy within lying and standing activities.

The top features include a mixture of frequency and time-domain features, suggesting differences in the variability and smoothness of movements for both standing and lying down between lame and non-lame sheep. In lame sheep, this could be an attempt by the animal to reduce discomfort caused by the lameness, whereby they redistribute their body weight to an unaffected leg leading to postural changes when standing. The research also suggests that lame sheep possibly lie differently than non-lame ones, this could once again be due to the animal’s attempt to alleviate the pain.

Article: Kaler, J., Mitsch, J., Vázquez-Diosdado, J. A., Bollard, N., Dottorini, T., Ellis, K. A. (2020). Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep. Royal Society Open Science, Article ID:190824, doi: 10.1098/rsos.190824

Article details

  • Date
  • 15 January 2020
  • Source
  • University of Nottingham