A single chip Photoplethysmography(PPG) sensor was developed for continuous measurements of heart rate from a mobile device. In order to utilize it in various mobile applications, it was necessary to achieve low power and small size of PPG sensors. For low power and small chip size of the PPG sensor, a photodiode(PD) for sensing signals and an analog front end(AFE) for signal amplification and ADC should be implemented in a single chip. The single chip PPG sensor which is implemented on a standard CMOS process with low operating voltage could be more suitable for mobile devices. In order to operate at a low voltage, reduction of Si thickness is required, and for this, high quantum efficiency(QE) of 43% at 940nm were obtained at 3um thickness by back side trench(BST) pattern and ARL optimization. In addition, to improve the performance of the PPG sensor, the leakage current of <0.1nA and capacitance of <200pF were measured by 20um pixel array. As a result, the low-power, small size single-chip PPG sensor showed similar performance to conventional high-voltage and large PPG sensor.
Photoplethysmography (PPG) is the detection of blood flow or pressure by optical means. The most common method involves direct skin-contact measurement of light from an LED. However, the small color changes in skin under normal lighting conditions, as recorded by conventional video, potentially allow passive, noncontact, PPG. Eulerian Video Magnification (EVM) was used to demonstrate that small color changes in a subject’s face can be amplified to make them visible to a human observer. A variety of methods have been applied to extract heart rate from video. The signal obtained by PPG is not a simple sinusoid, but has a relatively complex structure, which in video is degraded by ambient lighting variations, motion, noise, and a low sampling rate. Although EVM and many other analysis methods in the literature essentially operate in the frequency domain, fitting the video data to their model requires extensive preprocessing. In this paper a time-based autocorrelation method is applied directly to the video signal that exhibits superior noise rejection and resolution for detecting quasi-periodic waveforms. The method described in the current work avoids both the preprocessing computational cost and the potential signal distortions.
Photoplethysmography (PPG) is the detection of blood flow or pressure by optical means. The most common method involves direct skin-contact measurement of light from an LED. However, the small color changes in skin under normal lighting conditions, as recorded by conventional video, potentially allow passive, non-contact, PPG. A variety of methods have been applied to extract heartrate from such a video. In another paper submitted to this conference by the same authors, a new processing algorithm based on autocorrelation is shown to be effective without needing extensive video preprocessing to enhance the signal. That method was implemented using floating-point arithmetic in MatLab to analyze complete videos. However, the algorithm’s structure suggested that it might be possible to create a simplified, integer-only, approximation that is entirely incremental: updating a heart rate estimate as each frame is captured. This new, simplified, incremental algorithm allows a reprogrammed Canon PowerShot camera to function as a stand-alone, passive, non-contact, PPG device. The incremental integer algorithm and implementation are explained and evaluated in this paper.