GilHwan Kim

This study validates dynamic Bayesian optimization (DBO) for human-in-the-loop exoskeleton control, showing it improves walking speed and personalization compared to standard Bayesian optimization (BO).

This study compares dynamic Bayesian optimization (DBO) and Bayesian optimization (BO) in a human-in-the-loop system, finding DBO better handles non-stationary behavior in later training stages.

This study simulates human-in-the-loop optimization using Bayesian Optimization (BO) and Dynamic Bayesian Optimization (DBO) to assess DBO's effectiveness in scenarios involving motor learning, finding that DBO generally outperforms BO after sufficient iterations.

This paper validates existing models of motor learning in gait training and proposes a modified use-dependent learning model that achieves better fit with fewer parameters.