Gisu Heo  Yong Ki Son  YoungJun Kim  Changmok Oh  Dong-Woo Lee

10.5626/JOK.2026.53.2.148

wearable multi-sensor uncertainty-aware ensemble gait phase estimation temporalbagging and weighted fusion

This study introduces an uncertainty-aware Temporal Bagging Ensemble (UA-TBE) designed to robustly estimate continuous gait phases (0-100%) across various walking environments using wearable multi-sensor data, which includes an IMU, FSRs, and soft bending sensors. The proposed model achieves this by: (1) determining environment probabilities and domain uncertainty through a walking-environment classifier, (2) integrating predictions from sub-models trained with different temporal window ratios, and (3) quantifying both aleatoric and epistemic uncertainties, which are then combined using adaptive ensemble weighting. Utilizing a dataset collected at 100 Hz with 72-frame inputs across six walking environments (standing, level walking, ramp ascent/descent, and stair ascent/descent), UA-TBE demonstrated improvements of 69.8% in MAE, 71.1% in RMSE, and 2.5% in R² compared to a simple averaging ensemble. Furthermore, UA-TBE effectively mitigated overconfidence during transitions between environments and enhanced the contribution of high-performing models in high-certainty regions.