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Utilizing mobile phone cameras for continuous blood pressure (BP) monitoringpresents a cost-effective and accessible approach, yet it is challenged bylimitations in accuracy and interpretability. This study introduces fourinnovative strategies to enhance smartphone-based photoplethysmography for BPestimation (SPW-BP), addressing the interpretability-accuracy dilemma. First,we employ often-neglected data-quality improvement techniques, such as heightnormalization, corrupt data removal, and boundary signal reconstruction.Second, we conduct a comprehensive analysis of thirty waveform indicatorsacross three categories to identify the most predictive features. Third, we useSHapley Additive exPlanations (SHAP) analysis to ensure the transparency andexplainability of machine learning outcomes. Fourth, we utilize Bland-Altmananalysis alongside AAMI and BHS standards for comparative evaluation. Data from127 participants demonstrated a significant correlation betweensmartphone-captured waveform features and those from standard BP monitoringdevices. Employing multiple linear regression within a cross-validationframework, waveform variables predicted systolic blood pressure (SBP) with amean absolute error (MAE) of 3.08-16.64 mmHg and diastolic blood pressure (DBP)with an MAE of 2.86-13.16 mmHg. Further application of Random Forest modelssignificantly improved the prediction MAE for SBP to 2.61-15.21 mmHg and forDBP to 2.14-11.22 mmHg, indicating enhanced predictive accuracy. Correlationand SHAP analysis identified key features for improving BP estimation. However,Bland-Altman analysis revealed systematic biases, and MAE analysis showed thatthe results did not meet AAMI and BHS accuracy standards. Our findingshighlight the potential of SPW-BP, yet suggest that smartphone PPG technologyis not yet a viable alternative to traditional medical devices for BPmeasurement.

A Finger on the Pulse of Cardiovascular Health: Estimating Blood Pressure with Smartphone Photoplethysmography-Based Pulse Waveform Analysis