Technical article: published in Pig Progress
Author: Samaneh Azarpajouh
Preview: Lameness, a significant health and welfare concern in sow production systems, causes extensive financial losses in the swine industry. It is estimated that up to 48% of sows in a given farm are lame. Early and accurate lameness detection and treatment is critical to prevent economic losses and welfare issues.
Lameness can be evaluated using visual lameness score which is an easy to use and cheap to implement subjective method. However, visual scoring requires substantial training, it is time and labor involved, and prone to observe error due to human visual perception limitations. Objective measurement such as gait analysis is a uniform, sensitive, and definitive method to detect sow lameness. Gait analysis obtains quantitative measures of changes in gait quality and enables swine farmer to track the impacts of preventive treatments over an extended period of time. [...]What is gait analysis?
Gait analysis is an objective method to accurately assess gait deviation. In this method, different measurements of time and motion are recorded by higher definition cameras that capture higher frames per second to recognize lame and sound sows. Gait analysis parameters detect sows with gait abnormalities and issues before they visibly impair the sow.
Lame vs sound sow gait characteristics [...] In lame sows, there is a tendency of shorter stride lengths, lower velocity, and longer stance times than sound sows. In addition, there are variations in stride pattern due to pain in sows with poor limb structure.
What variables are used for lameness detection?
The most applied methods of gait analysis include kinematics and kinetics. Kinematics measures the movement geometry and calculates different gait aspects such as stride length. It also measures stance and swing duration and the forces causing the displacement or movement by measuring ground reaction forces with a force plate. Kinematic and kinetic analysis combined with behavioral change detection are used to develop a fully automated and continuous lameness detection system. Behavioral change parameters indicating lameness include uneven weight distribution among the legs, greater weight shifting among legs while standing, increased lying times and bouts, reduced walking speed, and changes in feeding behavior and activity.
