Data-Augmented Adaptive Oscillators: A Framework for Robust Gait Phase Estimation in Exoskeleton Assistance

Accurate and robust gait phase estimation is crucial for exoskeleton control in assistive walking. Existing data-driven techniques, while powerful, lack the predictability and generalizability needed for real-world use. Model-based methods, like adaptive oscillators (AOs), offer inherent certainty but cannot adapt to varying terrains. This study introduces a data-augmented adaptive oscillator (DA-AO), a hybrid framework synergizing mathematical models with the adaptability of learning-based perception. By dynamically retrieving the optimal hyperparameters for each detected locomotion mode, the DA-AO ensures that the oscillator consistently operates in its optimal regime, thereby achieving rapid adaptation across diverse walking scenarios. The proposed estimator is validated using a hip exoskeleton, achieving highly accurate estimation with root mean square errors (RMSEs) of 0.025, 0.021, and 0.032 for level-ground walking, ramp ascent, and stair ascent, respectively, during active assistive walking trials that effectively reduced average metabolic costs by 15.3%, 10.6%, and 5.7% ($p< 0.05$). Furthermore, the DA-AO system demonstrated robust gait phase estimation with an RMSE of 0.025 during continuous outdoor terrain transitions and reduced the cost of transport by 12.3% ($p< 0.05$) during outdoor hill walking. This work establishes a practical integration of data-driven adaptation within a model-based framework, demonstrating the potential of hybrid strategies to resolve the accuracy-reliability tradeoff and foster user trust in assistive walking with exoskeletons.