Smartphone-Based Photoplethysmography: A Signal Processing Approach for Low-Cost Cardiovascular Monitoring

This study presents a smartphone-based framework for photoplethysmography (PPG) signal acquisition and analysis, leveraging digital signal processing techniques to extract cardiovascular metrics. Using a smartphone camera and flashlight, fingertip videos were recorded and processed to obtain raw PPG signals through red channel intensity analysis. The signals were preprocessed using normalization and a Butterworth bandpass filter to suppress motion artifacts and isolate heart rate components. Peak detection algorithms were employed to extract inter-beat intervals, enabling the computation of heart rate and heart rate variability. Optional frequency-domain analysis was performed using the Welch method. The system demonstrates that with appropriate signal conditioning, reliable physiological parameters can be extracted from low-cost mobile devices. This approach highlights the potential of integrating real-time signal processing with ubiquitous hardware for accessible, non-invasive health monitoring applications, particularly in remote or resourcelimited settings. The model demonstrates strong predictive accuracy with low MAE (3.93), RMSE (4.34), and MAPE (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4. 9 6 %</tex>). A high correlation coefficient (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r=0. 9 6 7</tex>) against the readings of a pulse oximeter confirms reliable alignment with true PPG signals.