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Related Experiment Videos

Detecting lameness in sows from ear tag-sampled acceleration data using wavelets.

C Scheel1, I Traulsen1, W Auer2

  • 11Institute of Animal Breeding and Husbandry,Christian-Albrechts-University,Olshausenstr. 40,D-24098 Kiel,Germany.

Animal : an International Journal of Animal Bioscience
|April 11, 2017
PubMed
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This study developed an automated lameness detection system for sows using ear tag acceleration data. The system reliably distinguishes between lame and healthy pigs, enabling early intervention and improved animal welfare.

Area of Science:

  • Animal Science
  • Agricultural Engineering
  • Veterinary Medicine

Background:

  • Lameness is a significant welfare issue in group-housed sows, impacting productivity and health.
  • Early and reliable detection of lameness is crucial for timely intervention and treatment.

Purpose of the Study:

  • To develop an automated monitoring system for early and reliable lameness detection in group-housed sows.
  • To utilize acceleration data from ear tags for lameness detection.

Main Methods:

  • Acquired acceleration data from ear tags on sows over an extended period.
  • Applied wavelet transform to sow acceleration time series.
  • Computed feature series by estimating energy, variation, and variance within a moving window.
  • Decomposed feature series into uniform level sets and monitored highest/lowest levels for deviations from reference behavior.
Keywords:
accelerationdetectionlamenesssowwavelet

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Main Results:

  • The developed method clearly separated feature-activated levels between lame (mean 8.8) and healthy (mean 0.8) sows.
  • A threshold was established to avoid false positives, successfully classifying six out of seven lame sows within the last two days of observation.
  • Accuracy values reached up to 0.93 with an optimized threshold, demonstrating high classification performance.

Conclusions:

  • The ear tag-based acceleration data analysis system shows strong potential for feasible, individual lameness classification in sows.
  • The method can identify lameness without prior knowledge of the animal's health status.
  • Further validation with a larger dataset is recommended to confirm the system's robustness.