Power Optimization Using Embedded Automatic Gain Control Algorithm With Photoplethysmography Signal Quality Classification

This paper presents the design and implementation of an Automatic Gain Control (AGC) embedded algorithm for photoplethysmographic (PPG) sensors. We use a number of statistical and spectral characteristics of the raw and filtered PPG signals, referred to as multi-dimensional Signal Quality Metric (SQM) from the measured PPG signals on subjects' wrists. Using the Analog Devices (ADI) Vital Signs Monitoring (VSM) platform, and running the AGC in real time in an embedded ARM Cortex-M3 processor, we are able to save more than 50% LED power on average without compromising PPG signal quality needed for the VSM algorithms.