New global survey: 95% of orthopaedic vets say earlier detection could prevent more serious lameness cases
This summer we conducted a survey about equine lameness for more than 100 equine veterinarians from the ISELP community.
Sleip now measures over 100 anatomical key points on the moving horse
Sleip is proud to announce a significant update in our AI capabilities, enhancing user experiences and speeding up analysis. The upgraded neural network also opens the door for exciting product development opportunities.
At the heart of Sleip's technology is the innovative use of computer vision, a specialised sub-field of artificial intelligence (AI) that mimics human visual understanding – but far surpasses our cognitive capabilities. The new release catapults Sleip’s AI from about 20-30 to over 100 anatomical measurements in each frame of video.
Our AI now tracks 115 specific points on the horse's body in each frame of video footage, meaning roughly 350,000 measurements for an average trot up. These points act as markers, much like sensors attached to horses in gait analysis systems that employ physical equipment. And with this, we can measure movements rapidly in every video frame, says Felix Lawin, Computer Vision Researcher at Sleip.
Felix Järemo Lawin
PhD, Computer Vision Researcher
Vertical displacement of head and pelvis to measure asymmetries
Based on decades of equine biomechanical research, the gold standard for gait analysis that quantifies asymmetries is the measurement of the vertical displacement of the head and pelvis during a trot. However, by capturing a wider array of points, we get a more comprehensive view of a horse's movement pattern. This ensures the most accurate strides are chosen for analysis.
Enhanced tracking capability makes recordings easier
This comprehensive tracking allows Sleip to select the most optimal strides for gait analysis measurements. It ensures that our system can accurately understand and measure the horse's movement, even if there are some obstructions in the camera view, such as fencing when recording longeing in a round pen.
This next-generation neural network also sets the stage for future opportunities. With more detailed and holistic motion measurements, we can extract more data and detect more patterns from Sleip recordings. The potential to bring valuable insights for equine health and performance management is considerable, concludes Felix Lawin.
