  Utilizing mobile phone cameras for continuous blood pressure (BP) monitoring
presents a cost-effective and accessible approach, yet it is challenged by
limitations in accuracy and interpretability. This study introduces four
innovative strategies to enhance smartphone-based photoplethysmography for BP
estimation (SPW-BP), addressing the interpretability-accuracy dilemma. First,
we employ often-neglected data-quality improvement techniques, such as height
normalization, corrupt data removal, and boundary signal reconstruction.
Second, we conduct a comprehensive analysis of thirty waveform indicators
across three categories to identify the most predictive features. Third, we use
SHapley Additive exPlanations (SHAP) analysis to ensure the transparency and
explainability of machine learning outcomes. Fourth, we utilize Bland-Altman
analysis alongside AAMI and BHS standards for comparative evaluation. Data from
127 participants demonstrated a significant correlation between
smartphone-captured waveform features and those from standard BP monitoring
devices. Employing multiple linear regression within a cross-validation
framework, waveform variables predicted systolic blood pressure (SBP) with a
mean 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 models
significantly improved the prediction MAE for SBP to 2.61-15.21 mmHg and for
DBP to 2.14-11.22 mmHg, indicating enhanced predictive accuracy. Correlation
and SHAP analysis identified key features for improving BP estimation. However,
Bland-Altman analysis revealed systematic biases, and MAE analysis showed that
the results did not meet AAMI and BHS accuracy standards. Our findings
highlight the potential of SPW-BP, yet suggest that smartphone PPG technology
is not yet a viable alternative to traditional medical devices for BP
measurement.
