- The only mobile equine gait analysis system in the world that measures and produces reports in all gaits & all surfaces.
- The only mobile equine gait analysis system in the world that has sensors on the upper body & on the legs.
- It can measure in one measurement all gaits and events.
- It captures movements that might be close to impossible to detect with the naked eye.
- EquiMoves provides the best accuracy and precision on the market.
Wireless inertial sensors for capturing motion data are attached to the legs, the withers, the pelvis and the head of the horse. The sensor data is streamed to a gateway connected to the computer.
The EquiMoves motion processing software computes the relevant parameters and analyzes the horse’s gait for lameness and performance assessment.
In addition to the in depth analysis, the EquiMoves software offers basic or extended PDF reports for each test.
- Lameness exams
- Pre-purchase exams
- Sport horse monitoring
- Check if horses are fit to compete
- Analyze training progress
- Support of judges based on objective, quantitative equine gait analysis
- Support in selection of horses based on desired gait parameters
Request more information
If you are interested in using EquiMoves in your activities please send us a message and we will be happy to give you more information.
Tutorials & media gallery
Rosmark is a Dutch company providing custom gait analysis solutions for horses and support for equine project management.
Inertia Technology is a Dutch company specialized in the development of miniaturized wireless devices that can sense, process and communicate motion, vibration and orientation features of interest.
Founded in 1636, Utrecht University (Netherlands) is one of the largest research universities of Europe.
EquiMoves will be presented by Filipe Bragança on 21st of October at 19:00 during the Science of Equine Locomotion Symposium. The symposium is an online event taking place on 30th of September and 21st of October 2020. … Read more →
Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
F. M. Serra Bragança, S. Broomé, M. Rhodin, S. Björnsdóttir, V. Gunnarsson, J. P. Voskamp, E. Persson-Sjodin, W. Back, G. Lindgren, M. Novoa-Bravo, C. Roepstorff, B. J. van der Zwaag, P. R. Van Weeren & E. Hernlund, 20… Read more →