Machine Learning-Based VO2 Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO2) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO2 is a powerful prognostic predictor of survival in patients with heart failure (HF) because it provides an indirect assessment of a patient's ability to increase cardiac output (CO). In addition, VO2 measurements, particularly VO2 max, are significant because they provide a reliable indicator of your cardiovascular fitness and aerobic endurance. However, traditional VO2 assessment requires bulky, breath-by-breath gas analysis systems, limiting frequent and continuous monitoring to specialized settings. This study presents a novel wrist-worn multiwavelength photoplethysmography (PPG) device and machine learning algorithm designed to estimate VO2 continuously. Unlike conventional wearables that rely on static formulas for VO2 max estimation, our algorithm leverages the data from the PPG wearable and uses the Beer-Lambert Law with inputs from five wavelengths (670 nm, 770 nm, 810 nm, 850 nm, and 950 nm), incorporating the isosbestic point at 810 nm to differentiate oxy- and deoxy-hemoglobin. A validation study was conducted with eight subjects using a modified Bruce protocol, comparing the PPG-based estimates to the gold-standard Parvo Medics gas analysis system. The results demonstrated a mean absolute error of 1.66 mL/kg/min and an R2 of 0.94. By providing precise, individualized VO2 estimates using direct tissue oxygenation data, this wearable solution offers significant clinical and practical advantages over traditional methods, making continuous and accurate cardiovascular assessment readily available beyond clinical environments.