Validation of Commercial Wearables for Driver Monitoring: Assessing Heart Rate Accuracy and Reliability for Early Detection of Sudden Illnesses

19 real-world driving sessions, >18 h. Pulse rate from consumer wrist and ring PPG compared with ECG-derived HR. The ECG reference was cleaned with ALDTF + MODWT denoising to remove motion artefact and baseline wander without distorting morphology.

• Form factor: wrist

Sudden medical illnesses (SMI), including myocardial infarctions and occult arrhythmias, represent a primary etiology of catastrophic motor vehicle collisions. Although consumer wearable technologies provide a non-invasive modality for continuous driver monitoring, their measurement accuracy within dynamic automotive environments requires systematic evaluation. This investigation establishes an empirical validation paradigm for assessing the pulse-rate (PR) accuracy of consumer-grade wrist and finger photoplethysmography (PPG) devices against a reference heart rate (HR) value derived from an electrocardiographic (ECG) signal. To secure an uncompromised physiological reference, an analytical signal-processing pipeline was implemented, incorporating an Adaptive Layer-Dependent Thresholding Function (ALDTF) coupled with Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition to suppress high-frequency motion artifacts and baseline wander (BW) in the ECG signal without inducing morphological distortion. Nineteen real-world driving sessions, totaling over 18 h of continuous physiological telemetry, were evaluated via Lin’s Concordance Correlation Coefficient and Bland–Altman limit-of-agreement statistics. The findings demonstrate a distinct performance bifurcation governed by device form factor and internal data filtering. The evaluated smartwatch exhibited reliable tracking (MAE = 2.73 BPM) and preserved underlying autonomic variability, attaining distributional alignment with the ECG reference in 47% of trials. Conversely, ring-based form factors demonstrated substantial perfusion decoupling induced by isometric manual gripping forces, alongside aggressive proprietary smoothing filters that suppressed micro-fluctuations in heart rate variability (0% distributional alignment). These outcomes indicate that while wrist-worn form factors possess sufficient fidelity for detecting subclinical autonomic shifts, their implementation in automotive safety systems requires access to raw data to circumvent proprietary algorithmic filtering.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/

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