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High Sampling Rate Smartphone-PPG via built-in Rolling Shutter Image Sensor

IEEE Internet of Things Journal

Recent advancement of CMOS image sensor (CIS) on smartphone brings a significant improvement or benefits to IoT-based mobile healthcare technology in the form of CIS-photoplethysmography (CPPG). Nevertheless, most of the available smartphone in the market is equipped with a limited sampling rate (Fs), that is typically 30 frame per second (fps), thus often resulting in a distorted CPPG signal acquisition. This distorted signal is hard to be utilized for advanced clinical assessment, and only useful in a simple pulse rate monitoring system. In this paper, the rolling-shutter camera mechanism has been exploited to extract CPPG data points from CIS-pixel rows, thus allowing high-Fs CPPG signal extraction from a common built-in, low-fps smartphone CIS. Our novel measurement algorithm is called rolling-shutter CIS-PPG (RSCPPG), it consists of four main subparts including (i) temporal pixel-row based PPG extraction, (ii) pixel row to time information extraction, (iii) pixel row-PPG selection (PRS), and (iv) multi-row amplitude compensation (MRAC). Multiple experiments were conducted to prove the reliability of RSCPPG signal. Firstly, we conduct iterative experiments with different CIS parameters to find their correlation to the acquired RSCPPG signal quality. Results indicate that the short exposure time produces a high-SNR CPPG signal up to 25 ± 2.38 dB, and highly correlated signal morphology (average r = 0.95) compared to the reference PPG signal. Then, we also demonstrated the proposed RSCPPG algorithm allowing a high CPPG data sampling with Fs = 150 Hz (that is ≥ 5 times CIS fps). Finally, feasibility study has been conducted on multiple features extracted from RSCPPG that are potentially implemented for advanced clinical application. These findings suggest that the proposed RSCPPG algorithm is a reliable bio-signal acquisition technique in smartphone-based healthcare technology. This preliminary study also offers an insight into future research in smartphone-based CPPG for clinical application.

IEEE Internet of Things Journal (Early Access, 2022)