Zhaocheng Zhou

Perturbation-based training is essential for improving proprioception and reducing fall risk, yet many robotic platforms emphasize sagittal-plane assistance or apply three-dimensional perturbations via trunk, and configurable multidirectional perturbations directly at the foot-ankle interface during walking are still lacking. In this study, we present a robotic system for proprioceptive training that programmatically delivers plantar perturbations in multiple directions during gait. The system integrates a 10-DoF series-parallel hybrid mechanism, an immersive visual interface, and a suite of task modes to reproduce unstable terrains and generate controlled perturbations. To ensure stable operation, a composite control method combining iterative learning with neural-network disturbance estimation was designed, and reduced the trajectory-tracking errors by over 60% in comparison with PID and sliding mode control. Experiments involving ten healthy participants were conducted under mediolateral perturbations and sEMG-based muscle synergy analysis revealed training-induced neuromuscular adaptations, marked by the progressive activation of key frontal-plane balance muscles - gluteus medius, tensor fasciae latae, and peroneus longus - and a reorganization of task-specific synergies under higher perturbation levels. These findings demonstrate that the elaborated proprioceptive training with controlled perturbations drives neuromuscular adaptation, highlighting the potential of the proposed robotic system as a viable tool for balance rehabilitation.