A 2D stick model for simulation of sow walking on concrete floors and detection of sow lameness. (February 2023) - Yan, Xiaojie; Zhang, Qiang; Connor, Laurie; Devillers, Nicolas; Dick, Kristopher
Abstract : A 2D stick model was developed to simulate sow walking on concrete floors. The model consisted of thirteen connective body joints to form a simple stick figure that approximates a sow's body skeleton. The video images of sows with reflective markers on their bodies walking along a corridor of concrete floor were analysed and translated into a 2D coordinate dataset. The motion of each landmark, including displacement, velocity and acceleration were calculated. A step of walking was modelled as three consecutive phases of motion: acceleration of foot leaving the ground; constant speed of foot swinging; and deceleration of foot touching the ground. One gait cycle was separated into several time steps and the movements of each joint at a time step in the horizontal (x) and vertical (y) directions were described by a combination of first and 2nd order polynomial functions. The coordinates of all joints were calculated in the x- and y-directions and these joints were then linked by synchronizing the motions of each joint at a given time step. The joint coordinates at each time step were then used to calculate a selected set of characteristic gait parameters, including the stride length and stance time for fore- and hindlimbs, diagonality, back angle and walking speed. Based on the comparisons of the predicted gait parameters between the lame and non-lame sows, the criteria were established for using the predicted gait parameters to detect sow lameness. The results showed an accuracy greater than 92% for lameness detection. Highlights: A 2D stick model was developed to simulate sow walking on concrete floors. The model consisted of 13 joints and 12 sticks to represent a sow skeleton. Joint movements were modelled by a 2nd order polynomial or a linear function. The model predicted a set of parameters to characterize sow gait. The model was used to detect lame sows with an accuracy over 92%.