Low-Rate Wrist SpO2 Estimation under Micro-Perturbations Using Motion-Aware Beat Selection and Perfusion-Guided Calibration

Zequan Liang, Ning Miao, Wei Shao, Mahdi Pirayesh Shirazi Nejad, Ehsan Kourkchi, Hossein Sayadi, Setareh Rafatirad, Houman Homayoun

Continuous oxygen saturation (SPO2) monitoring from photoplethysmography (PPG) is important for wearable health sensing, but wrist-based SPO2 estimation remains challenging due to subtle wrist micro-perturbations and inter-subject differences in local perfusion status. These factors can destabilize the red-to-infrared ratio-of-ratios (R) and reduce the reliability of conventional fixed R-SPO2 mapping. This paper proposes a lightweight low-rate wrist SPO2 estimation framework that integrates motion-aware beat selection and perfusion-guided calibration. The proposed method extracts beat-level alternating-current/direct-current (AC/DC) components from dual-wavelength PPG signals, computes beat-level R values, and uses accelerometer-derived motion scores to weight beats within each sliding window. A subject-specific perfusion reference is further used to guide calibration across different perfusion conditions. Experiments on a private wearable dataset show that the proposed method achieves the best 25 Hz performance, with an MAE of 2.305$\pm$1.113% and an RMSE of 3.117$\pm$1.743%, while maintaining performance comparable to the 100 Hz sampling rate and reducing PPG sensor power consumption for energy-efficient wearable implementation. These results demonstrate the effectiveness of the proposed framework for low-rate wrist SPO2 estimation under micro-perturbations.