Explainable and Subject-Independent VO₂ Estimation Using a Single IMU: A Lightweight Ensemble Framework Under LOSO...

Continuous estimation of oxygen uptake (VO₂) using wearable inertial sensors offers a practical alternative to laboratory-based metabolic testing but remains challenging due to the indirect relationship between kinematics and physiological demand. This study presents a lightweight two-stage pipeline for simultaneous heel-strike (HS) detection and VO₂ estimation using a single calf-mounted IMU. In Stage 1, an Extreme Learning Machine (ELM) + Random Forest (RF) ensemble achieves the highest HS detection F1-score (0.818) under leave-one-subject-out (LOSO) validation, outperforming a temporal convolutional network (TCN) deep learning baseline (F1 = 0.674), which exhibited higher variability across subjects. In Stage 2, kinematic and gait-derived features from 30 s windows are used to estimate normalized VO₂ via RF and ensemble regression under LOSO cross-validation across 24 participants. The RF model achieves a median R² of 0.687 using predicted HS (Pred-HS) events and 0.679 using ground-truth (GT) annotations, with the ensemble showing similar performance (median R² ≈ 0.675-0.691). No statistically significant difference was observed between GT-HS and Pred-HS conditions (p > 0.05). SHAP analysis identifies accelerometer variability (acc_std) and gyroscope-derived features as dominant predictors, with demographic variables contributing minimally. Overall, the results suggest that VO₂ estimation may be achieved using automatically detected gait events without manual annotation. The proposed pipeline is computationally efficient and indicates feasibility under controlled conditions, subject to further validation.