Automatic lameness detection based on consecutive 3D-video recordings

T. van Hertem, S. Viazzi, M. Steensels, E. Maltz, A. Antler, V. Alchanatis, A. Schlageter-Tello, C. Lokhorst, C.E.B. Romanini, C. Bahr, D. Berckmans, I. Halachmi

    Research output: Contribution to journalArticleAcademicpeer-review

    58 Citations (Scopus)

    Abstract

    Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive night-time milking sessions on an Israeli dairy farm, using a 3D-camera. A live on-the-spot assessed 5-point locomotion score was the reference for the automatic lameness score evaluation. A dataset of 186 cows with four automatic lameness scores and four live locomotion score repetitions was used for testing three different classification methods. The analysis of the automatic scores as independent observations led to a correct classification rate of 53.0% on a 5-point level scale. A multinomial logistic regression model based on four individual consecutive measures obtained a correct classification rate of 60.2%. When allowing a 1 unit error on the 5-point level scale, a correct classification rate of 90.9% was obtained. Strict binary classification to Lame vs. Not-Lame categories reached 81.2% correct classification rate. The use of cow individual consecutive measurements improved the correct classification rate of an automatic lameness detection system.
    Original languageEnglish
    Pages (from-to)108-116
    JournalBiosystems Engineering
    Volume119
    DOIs
    Publication statusPublished - 2014

    Keywords

    • dairy-cattle
    • risk-factors
    • milk-yield
    • clinical lameness
    • gait assessment
    • scoring system
    • back posture
    • cows
    • locomotion
    • herds

    Fingerprint Dive into the research topics of 'Automatic lameness detection based on consecutive 3D-video recordings'. Together they form a unique fingerprint.

  • Cite this

    van Hertem, T., Viazzi, S., Steensels, M., Maltz, E., Antler, A., Alchanatis, V., ... Halachmi, I. (2014). Automatic lameness detection based on consecutive 3D-video recordings. Biosystems Engineering, 119, 108-116. https://doi.org/10.1016/j.biosystemseng.2014.01.009