Peak-Independent Cuffless Blood Pressure Monitoring Using a Smart Sock: The Role of Temporal Lag Modeling in Foot-Based PPG

TL;DR

A smart sock uses foot-based PPG with temporal lag modeling to enable cuffless and continuous blood pressure monitoring with high accuracy after calibration.

Contribution

Introduces a peak-independent framework using foot-based PPG and temporal lag modeling for cuffless BP monitoring.

Findings

System achieved 8.60 mmHg systolic and 6.42 mmHg diastolic BP errors in subject-independent estimation.

With calibration, mean absolute errors dropped to 1.3–1.7 mmHg with R2 > 0.90.

Temporal lag modeling captures hemodynamic memory without requiring pulse peak detection.

Abstract

Continuous blood pressure (BP) monitoring remains a major challenge in wearable healthcare systems, as conventional cuff-based sphygmomanometers are intermittent and unsuitable for long-term use. This study presents a Smart Sock platform for cuffless BP estimation using single-site photoplethysmography (PPG). Unlike approaches based on pulse transit time or fiducial point detection, the proposed framework relies on peak-independent features extracted from PPG and its first and second derivatives, capturing blood volume and hemodynamic dynamics in the lower limb. PPG signals from 60 participants were segmented into overlapping 30 s windows and processed through a unified preprocessing pipeline. A compact set of physiologically meaningful statistical and information-theoretic features was extracted from each window, and temporal lag modelling (5–15 s) was employed to encode short-term hemodynamic memory without explicit peak detection. Multiple regression models were assessed using leakage-safe cross-validation strategies. In a subject-independent diagnosis scenario, the system achieved errors of 8.60 mmHg for systolic BP and 6.42 mmHg for diastolic BP. In a monitoring scenario with single-point calibration, performance substantially improved, yielding mean absolute errors of 1.3–1.7 mmHg and R2 > 0.90. These results demonstrate that foot-based PPG, combined with peak-independent feature engineering and temporal context modeling, enables accurate and comfortable continuous personalized blood pressure monitoring after calibration, while subject-independent estimation remains more challenging.

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