Hearing Your Calories: Short-Session Calibrated Energy Expenditure Monitoring via Earable Respiratory Sensing

Accurate monitoring of energy expenditure (EE) during exercise is crucial for health management, fitness optimization, and clinical assessment. Although many commercial wearables offer EE levels as a standard feature, they typically rely on simple regressions of heart rate, motion trajectory, or activity intensity. Consequently, such models inherently struggle with cross-activity generalization and demand massive labeled data, leading to significant accuracy degradation in practical deployment. To address this, we propose EarEE, a novel earable system that estimates EE by decoding exercise-associated respiratory sounds into oxygen consumption and carbon dioxide production, which are gold-standard metabolic indicators of EE. EarEE advances existing technologies through three key innovations: i) Dynamics-Aware Deep Modeling , which captures the nonlinear temporal and contextual relationship between respiratory acoustics and gas exchange; ii) Generative Data Synthesis , which expands data from a short calibration session into diverse and realistic respiratory profiles, enabling robust model training with limited labeled data; and iii) Heart Sounds Suppression , which mitigates heart sound artifacts for reliable estimation during intensive activities. Extensive evaluations with 42 participants across 7 exercises demonstrate that EarEE achieves an average relative error of 10.95%, significantly outperforming wearable solutions and approaching the 10% clinical accuracy benchmark, highlighting its potential for practical daily EE monitoring.