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

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Sensor IC Design

Background:

• Photoplethysmography (PPG) is crucial for wearable health monitoring.
• Existing PPG sensors face challenges with power consumption and motion artifacts.
• Accurate heart rate and SpO2 monitoring requires robust signal processing.

Purpose of the Study:

• To develop an energy-efficient PPG sensor IC with adaptive windowing for low-power wearable devices.
• To improve the accuracy of PPG signal analysis, particularly under motion-degraded conditions.
• To reduce the power consumption of PPG sensing systems.

Main Methods:

• Utilized the first derivative of the PPG signal (velocity of PPG, VPG) for adaptive windowing.
• Developed an algorithm to track PPG peaks and valleys (PAVs) enabling dynamic sampling rates.
• Fabricated the sensor IC using a 180-nm CMOS process and validated with in vivo measurements.

Main Results:

• Reduced the PAV missing rate from 9.90% to 3.81% compared to period-based algorithms.
• Achieved mean absolute errors of 0.73 bpm for heart rate and 0.53% for SpO2.
• Reduced total power consumption to 10.42 μW per channel (53% reduction).

Conclusions:

• The proposed VPG-based adaptive windowing method offers a practical solution for low-power PPG sensing.
• The developed sensor IC demonstrates high accuracy and significant power savings for wearable monitoring.
• This technology enables more efficient and reliable continuous health tracking.