An adaptive PPG (Photoplethysmography) readout system for a dual-channel OLED-OPD flexible sensor is designed and developed with motion artifact (90dB (1nA to 100 µA) and input-referred noise of 0.26 nA/√H, with power consumption of 460µW. The DC drift is successfully reduced to 1% of its average. The accuracy for heart rate is 96%.

A new approach for estimating blood pressure from photoplethysmography (PPG) signals is developed using artificial neural networks (ANNs). Blood Pressure is one of the most important parameters that can provide valuable information of personal healthcare. A reflective photoplethysmography (PPG) sensor module is developed for the cuffless, non-invasive blood pressure (BP) measurement based on PPG at wrist on radial artery. Blood Pressure is in a relation with the pulse duration of the PPG. In this paper, we propose to estimate blood pressure from PPG signal by using artificial neural networks approach. This is the first reported study to consider varied temporal periods of PPG waveforms as features for application of artificial neural networks (ANNs) to estimate blood pressure. We compared our results with those measured using a commercial cuff-based digital blood pressure measuring device and obtained encouraging results of overall SBP and DBP regression (R) as 0.99115.

This study proposes a new multiwavelength OLED-OPD flexible PPG sensor with a time-interleaved, optimal high order adaptive analog filter-based readout system for the longtime continuous monitoring of PPG signal. First time, temperature modulation of the light absorption and scattering coefficient of the human skin and its compensation strategy has been explored in this study. This study intended to minimize the issue like motion artifacts and mispositioning. Analog front end of the readout circuit incorporates a transimpedance amplifier, optimal filter, programmable gain amplifier (1x to 7x), PWM-DAC based OLED intensity control, Bluetooth transceiver, MCU with inbuilt ADC and a power management unit. At the backend, digital control and pre-signal processing-algorithm are developed to improve the adaptability of the readout system against nonidealities which enable the readout system for the longtime continuous measurement of the biological sign. Digitally variable resistance is used herein to tune the central frequency of the bandpass filter which helps to minimize low heart rate issue. The sensing range of the readout circuit is 20nA to 10µA. With 3.3 V power supply the total power consumption of the readout system is 0.4W. Experimental result shows that the heart rate measurement error is less than 3.4bpm. In the worst case, the measured repulsive pulse transit time (RPTT) variation from PPG cycle to cycle is less than 2%.