Activity-Independent Estimation of VO2max from Short-Duration Multimodal Wearable Signals

Cardiorespiratory fitness is a key indicator of overall health, yet its assessment still largely depends on structured protocols such as cardiopulmonary exercise testing (CPET), which require specialized equipment, trained personnel, and controlled laboratory conditions that limit accessibility. Wearable sensing technologies offer a practical alternative by continuously capturing physiological and biomechanical signals during daily life. However, most wearable-based approaches remain constrained by activity-specific modeling, structured exercise protocols, or prolonged monitoring periods, limiting generalization across real-world behaviors. This work presents an activity-independent machine learning framework for estimating VO2max from short-duration multimodal wearable signals acquired during semi-structured real-world daily activities. The proposed two-stage framework first estimates the metabolic equivalent of task (MET) as a continuous representation of activity intensity, then integrates this estimate with physiological, biomechanical, and demographic features to predict subject-level VO2max. By decoupling physiological demand from explicit activity labels, the framework improves robustness to unseen activities while preserving physiological interpretability. Evaluation under the Leave-One-Subject-Out validation protocol demonstrates that short-duration wearable-derived signals encode meaningful information related to inter-subject differences in cardiorespiratory fitness. These findings support the feasibility of activity-independent, wearable-based fitness estimation and provide a practical foundation for scalable preventive health monitoring in everyday life.