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Evaluation of lameness detection using radar sensing in ruminants
  1. Valentina Busin1,
  2. Lorenzo Viora2,
  3. George King2,
  4. Martin Tomlinson2,
  5. Julien LeKernec3,
  6. Nicholas Jonsson4 and
  7. Francesco Fioranelli3
  1. 1 Division of Pathology, Public Health and Disease Investigation, School of Veterinary Medicine, University of Glasgow, Glasgow, UK
  2. 2 Farm Animal Cinical Sciences, School of Veterinary Medicine, University of Glasgow, Glasgow, UK
  3. 3 School of Engineering, University of Glasgow, Glasgow, UK
  4. 4 Institute of Biodiversity, Animal Health and Comparative Medicine, Glasgow, UK
  1. Correspondence to Dr Valentina Busin; Valentina.Busin{at}


Background Lameness is a major health, welfare and production-limiting condition for the livestock industries. The current ‘gold-standard’ method of assessing lameness by visual locomotion scoring is subjective and time consuming, whereas recent technological advancements have enabled the development of alternative and more objective methods for its detection.

Methods This study evaluated a novel lameness detection method using micro-Doppler radar signatures to categorise animals as lame or non-lame. Animals were visually scored by veterinarian and radar data were collected for the same animals.

Results A machine learning algorithm was developed to interpret the radar signatures and provide automatic classification of the animals. Using veterinary scoring as a standard method, the classification by radar signature provided 85 per cent sensitivity and 81 per cent specificity for cattle and 96 per cent sensitivity and 94 per cent specificity for sheep.

Conclusion This radar sensing method shows promise for the development of a highly functional, rapid and reliable recognition tool of lame animals, which could be integrated into automatic, on-farm systems for sheep and cattle.

  • Lameness
  • radar-sensing
  • Dairy cattle
  • Sheep
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  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the study are included in the article.

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