Synchronization dynamics between rider and saddle motion across gaits revealed by IMU analysis

Abstract

Background: Effective rider–horse coordination is essential for performance and welfare, yet objective, field-based quantification of rider–saddle synchronization across gaits remains limited.

Aims/objectives: To quantify dynamic synchronization between rider motion and saddle motion (as a proxy for horse trunk oscillations) across walk, trot, and canter using a minimal inertial measurement unit (IMU) setup, and to determine whether synchronization differs between rider body segments.

Methods: Two experienced riders (8–20 years riding experience) rode their own or familiar warmblood horses (n = 2; age 6–14 years) at walk, trot, and canter under collected, medium, and extended riding frame (collection level) conditions. Four wireless inertial measurement units (pelvis, trunk, head, saddle) recorded tri-axial acceleration at 60 Hz. Vertical acceleration of rider segments was analyzed relative to saddle motion. Synchronization was quantified using cross-correlation coefficients, phase lag, and phase-locking values (PLV). Mean values were calculated across repeated trials.

Results: Synchronization strength increased with gait speed. Mean cross-correlation coefficients between pelvis and saddle were 0.64 at walk, 0.92 at trot, and 0.96 at canter, with corresponding mean phase lags of 22 ms, 0 ms, and 0 ms, respectively. Trunk and head segments showed lower synchronization at walk (r = 0.62–0.65) but high coupling at trot (r = 0.88–0.93) and canter (r = 0.91–0.95). Mean PLV values increased from 0.52 to 0.55 at walk to 0.91–0.95 at trot and 0.92–0.94 at canter. Riding frame condition influenced synchronization primarily at walk, with lower mean correlation and PLV in the extended frame.

Conclusion: Within this population, rider–saddle synchronization increased with gait speed and was strongest at the pelvis, supporting its role as the primary mechanical interface transmitting horse trunk motion to the rider. A minimal IMU-based approach captured repeatable, segment-specific synchronization patterns under field conditions.