An Ultralow-Power PPG Sensor With Adaptive Predictive Sampling

Photoplethysmography (PPG) sensors provide precise vital sign measurements. However, conventional PPG signal detection requires continuous light-emitting diodes (LEDs), resulting in significant power consumption. To cancel dc components, ambient light, and motion artifacts (MAs), PPG sensors incorporate significant signal processing circuitry. This circuitry is critical to cancel interference, increase dynamic range (DR), and improve signal-to-noise ratio (SNR), which further increases power consumption. This article presents a novel PPG sensor that adaptively predicts and samples only the peaks and valleys (PAVs), i.e., the maximum and minimum amplitudes, of the PPG signal. Using the predicted PAVs, the sensor extracts vital signs, including heart rate (HR), HR variability (HRV), oxygen saturation (SpO2), systolic, diastolic, and cardiac cycles, while reducing power consumption by over 87%. Our sampling scheme shows a low mean absolute error (MAE) of 5.76 beats per minute (bpm) when tested on the PPG-DaLiA dataset with data from 15 subjects. Moreover, in a prototype test with five subjects, our sensor was used on various body parts, including the finger, wrist, arm, chest, neck, earlobe, and forehead, during activities, such as walking, typing, and sitting. Achieved MAEs are <0.5 bpm for HR, <1% for SpO(2), and <3 ms for HRV, systolic, diastolic, and cardiac cycles, across all subjects.