CaReFit: Cost-Effective Cardiorespiratory Fitness Assessment Using Multimodal Sensors

Cardiorespiratory fitness (CRF) is crucial for maintaining overall health, with VO 2 max (maximal oxygen consumption) and VO 2 AT (oxygen consumption at anaerobic threshold) serving as key physiological indicators. The current gold standard for assessing these metrics is Cardiopulmonary Exercise Testing (CPET), which requires bulky equipment, trained personnel, and significant time, putting it out of reach for most people. In this study, we introduce CaReFit, an innovative and low-cost system that utilizes a single-lead electrocardiogram (ECG) and muscle oxygenation (SmO 2 ) sensors to estimate VO 2 max and VO 2 AT. Our method leverages the coupled behavior of ECG and SmO 2 signals during exercise to predict these critical performance metrics. To mitigate exercise-induced noise in ECG signals, we implement a diffusion-based ECG denoising model trained with a tailored ECG-guided loss function on combined public datasets. Then, our multimodal neural network is specifically designed to leverage physiological insights for accurate CRF predictions. We conducted a deployment of CaReFit with 50 patients in a clinical setting, and evaluated CaReFit under a strict subject-disjoint protocol. Under this leakage-free evaluation, CaReFit achieves a mean absolute percentage error (MAPE) of 9.30% (95% CI: 7.06-11.80) for VO 2 max and 9.49% (95% CI: 7.02-12.12) for VO 2 AT. We further conduct a head-to-head comparison with a commercial portable device, and our system demonstrates similar VO 2 max performance trends under the same session protocol while additionally supporting VO 2 AT estimation. Furthermore, we make our dataset and evaluation code publicly available to facilitate further research and development in this domain.