My sister and I founded EcoLogic Health to find ways to explore the interconnected world of humans, animals, and the environment. We have organized eco-friendly fundraisers and food drives to help support those in need, and we have started research on inflammatory diseases, specifically in the potential causal impacts the environment may have on the development of such conditions. One of our other important prerogatives is to find solutions for the animal world, as well. Recently, I thought of an interesting concept that inspired me to open our newest EcoLogic initiative – developing a sensor system to detect injury in horses.
Whether a hobbyist or a competitor, one of the most important aspects of being an equestrian is protecting the health of your horse. One of the challenges to fulfilling that obligation is that horse and rider can’t communicate verbally about what’s wrong or what hurts. For example, veterinarians and trainers can observe how a horse walks, trots, or canters and establish with some certainty whether or not the horse is lame, but without the horse providing input earlier in the state of the injury, diagnostic time is squandered, and the horse may even be subjected to unnecessary pain or discomfort. Absent complex verbal or written communication of symptoms, how can we solve for this?
As an equestrian, I’ve thought about what indicators an observer would look for to determine lameness, and wondered what other method could be applied to receive these data directly from the horse. As a matter of course, horses wear what are called “boots” when practicing, to protect them from kicking their own legs as they move. The signs of lameness are usually discovered when a horse visibly favors a sore leg. Equine boots could therefore be fitted with sensors to provide objective data demonstrating the soundness of each leg, perhaps even before the effect would be observed, visually.
In our research, we examined whether accelerometer data could be used in conjunction with a coupled nonlinear dynamics model to detect mode interactions and abnormal gaits in equines. Coupled oscillators are commonly found in nature, primarily in quadrupedal locomotion that is controlled by a central pattern generator (CPG) capable of producing rhythmic gaits, including walk, trot, canter, and gallop. By designing an accelerometer system for equine boots, we were able to monitor different types of equine motion to look for signs of lameness. We collected multi-point motion data and then compared these data to equine motion output from a model we developed called the DYnamics Model for Equine Movement (DYMEM). Analysis of the spectral content and symmetry of variance revealed a characteristic pattern that identified abnormal motion.
With sensor-equipped boots, it will be possible for riders to receive data directly through Bluetooth or a WiFi connection, updating them in real-time whenever their horse is exercised or ridden, and providing immediate feedback for possible pain or injury before it develops into a chronic condition or requires surgical intervention to correct. My goal is to developing engineering solutions to improve the quality of life for ALL beings, whether two-legged or four.