A VPG-Based Adaptive Windowing PPG Sensor IC for Low-Power Wearable Monitoring

This paper presents an energy-efficient photoplethysmography (PPG) sensor IC with adaptive windowing for low-power wearable monitoring. By utilizing the first derivative of the PPG signal (i.e., the velocity of PPG, VPG), the proposed algorithm tracks the PPG peaks and valleys (PAVs), enabling higher sampling rates only when necessary. Validated on the BIDMC dataset, the proposed VPG-based method reduces the PAV missing rate from 9.90% to 3.81% compared with the period-based algorithm. Additional BIDMC evaluations under additive noise and periodic artifact injection characterize its operating boundary, while representative walking examples from the PTT dataset illustrate PAV tracking under real motion-degraded conditions. The proposed IC, fabricated in a 180-nm CMOS process, is validated through in vivo measurements against a clinical-grade reference device. The system achieves mean absolute errors (MAEs) of 0.73 bpm for heart rate and 0.53% for SpO₂, while reducing the total power consumption to 10.42 μW per channel, corresponding to a 53% reduction. These results demonstrate a practical low-power PPG sensing solution for wearable monitoring.