Showjumping is a gripping sport that involves negotiating a series of daunting obstacles, including high walls and water jumps. It demands razor-sharp precision as riders assess each challenge with the intensity of a chess master. As for the horse, there are subtle changes of rhythm, shortening of the stride, and shifts in balance. But things can still go horribly wrong, as misplaced hooves bring down a rail or the horse loses its nerve and veers away from a jump.
So how to improve overall performance for both riders and horses? APQX, the high-performance simulation and optimization company, is mounting a convincing case for AI as the key to better outcomes in showjumping. In doing so, it’s lifting the curtain on a whole new generation of artificial intelligence—to be driven by physics-based AI.
This is a form of machine learning where AI systems not only absorb huge amounts of data but integrate the laws of physics to predict real-world behavior. APQX combines advanced engineering, scientific machine learning, and optimization techniques to help understand and improve complex systems in sectors such as aerospace and automotive, but also equestrian biomechanics. Or, put another way, what works for horses can also be applied to other high-performance systems and vehicles.
Showjumping is an intriguing sport as it involves two athletes working closely together, but only one is capable of verbal communication. And yet these partners must find a way to understand each other. It seems that AI can help enable that critical complex interaction.
To achieve this, APQX has unveiled StrydeUp, a physics-based performance analytics platform applying machine learning and computer vision capabilities to the equestrian showjumping market launched at the Megève equestrian event. It promises to be the first of a portfolio of similar offerings across different sectors and sports—a point made forcefully by Greg Tallant, president of APQX: “For decades we’ve relied on sensors to tell us how complex systems behave. Today, advances in AI and machine learning mean we can extract that same kind of insight from ordinary observations, including video. Once you can transform unstructured information into structured intelligence, the possibilities extend far beyond any single application.”
So APQX’s offerings can be used in high-performance sport, but also, going forward, areas such as aerospace, automotive, and other sectors. These include video-based machine learning (VideoML), extracting physics from footage to predict future action.
Let’s break down these offerings. Physics AI accelerates the system design and optimization process for complex engineering applications such as aircraft and marine vessels. Design ML accelerates the engineering design process—optimizing designs within a system's parameters. And Decision AI sharpens both pre-event strategy and real-time, in-the-moment decisions in competitive environments.
The process of collecting data from a remote source and transmitting it to another location for monitoring, referred to as telemetry, has been used by NASA, motorsport, in competitions such as the America’s Cup, and in determining football strategy. The beauty of StrydeUp is deploying telemetry without hardware attached to the horse and rider, instead drawing on video-derived data.
For APQX, showjumping has provided a perfect laboratory for this next phase of AI, testing and proving the new concepts. StrydeUp converts film footage from smartphones or cameras into detailed biomechanical assessments. There’s no need for sensors, wearable devices, or specialist hardware. Video of the showjumping action can be used to provide actionable data on stride, symmetry, and jump mechanics in minutes. This information is used by owners, trainers, riders, and veterinarians to vastly improve the performance of their horses and riders.
APQX has brought on board top showjumping practitioners as advisers. Nathan Budd, a member of the Belgian national team, provides strategic guidance and access to his stable for testing. While Morgane Dassio, a former Swiss U21 champion, is helping to expand APQX’s real-world testing pool. She has spoken of the need to “use cutting edge technology to achieve dramatic progress in our wonderful sport”.
Budd expresses his enthusiasm for the potential of the new platform: “The data on which we’ve built StrydeUp addresses something I’ve felt my entire career. Instinct alone isn’t enough at the highest level. Now we can actually see what’s happening.”
With StrydeUp, APQX has successfully differentiated itself from the competition. Harnessing SciML and computer vision, it’s capturing joint mechanics, ground-reaction forces, and momentum in a way that protects both the rider and horse while making equestrian sport an even more thrilling spectacle. And in the process, it’s further enhancing a suite of AI-based tools that can be rolled out across a wide range of high-performance environments.
To learn more about APQX, visit apqx.com
To find out how showjumping is showcasing next level AI, visit strydeup.com
