Biomechanical studies of locomotion in pigs

Abstract

Lameness is a major cause of lost productivity for the pig industry. The objective of this PhD was to develop an objective motion capture method for growing pigs and assess (1) the repeatability and sensitivity of the method (2) the gait characteristics of pigs housed on different floor types and (3) gait differences in pigs with conformational deficiencies, joint disease and/or clinical lameness. Infrared camera-based motion capture was applied to three different cohorts of pigs in three experiments, including an observational study following 84 gilts from grower- to second-parity stage. 3D coordinate data of reflective skin markers attached to head, neck, trunk and leg anatomical landmarks were collected. Temporal (time), linear (displacement) and angular (joint angles) kinematic gait parameters were calculated. Repeatability of the method varied with amount of overlying tissue and/or prominence of anatomical landmarks used for marker placement, but not necessarily with walking speed. Gait development of pigs reared on fully-slatted, partly-slatted or deep straw-bedded floors was not different. Lameness detection and evaluation was possible using relative linear and temporal kinematics. The within-stride trajectory of head and pelvic regions during walking differentiated pigs with front and multi-leg lameness from normal pigs, respectively. The ipsilateral swing-to-stance time ratio detected lameness in hind legs, but was not affected during multi-leg lameness. The frequency and magnitude of irregular steps was increased in lame pigs and in pigs with subclinical joint lesions of osteochondrosis diagnosed post slaughter. Step irregularity (as reflected in the step-to-stride length ratio) was also predictive of impending lameness. The step-to-stride length ratio is a dimensionless and ideal parameter to monitor pigs of different age and size, moving at a self-chosen walking speed. Flexion asymmetry and joint flexion patterns were indicative of locomotor problems in some cases. Gait analysis therefore offers potential for automated prediction and early detection of lameness.