User-Adaptive Variable Impedance Control Using Bayesian Optimization for Robot-Aided Ankle Rehabilitation

This paper presents a user-adaptive variable impedance control approach for robot-aided rehabilitation, initially focusing on an ankle rehabilitation application. The controller dynamically adjusts the impedance parameters based on the user's motion intent, thereby providing personalized assistance during motor tasks. Bayesian optimization is employed to enhance speed and accuracy during the motor tasks by minimizing an objective function formulated from the user's kinematic data. The optimization process incorporates a Gaussian process as a surrogate model to address uncertainties inherent in human behaviors. Furthermore, an outlier rejection method based on the Student-t process is integrated into Bayesian optimization to enhance its robustness. To evaluate the effectiveness of the proposed control approach, a goal-directed target-reaching study was conducted with 15 healthy participants using a wearable ankle robot. The performance metrics of speed, accuracy, task completion time, and user effort were used to compare the optimized variable impedance controller against an unoptimized counterpart. Results showed that the optimized controller achieved an average speed improvement of 9.9% and a 7.6% decrease in deviation from the target trajectory compared to the unoptimized controller. Additionally, the optimized controller reduced task completion time by 6.6% while maintaining a similar level of user effort. Notably, the optimal parameters for each individual varied significantly, highlighting the significance of the user-adaptive approach. Overall, this study demonstrates the effectiveness and feasibility of the proposed optimal variable impedance control approach for robot-aided rehabilitation applications, particularly in the context of ankle rehabilitation.