Signal Quality Assessment and Reconstruction of PPG-Derived Signals for Heart Rate and Variability Estimation in In-Vehicle Applications: A Comparative Review and Empirical Validation

Gao Ruimin; Carl Miller; Brian T W Lin; Chris Schwarz; Monica L H Jones

doi:10.3390/s25247556

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Signal Quality Assessment and Reconstruction of PPG-Derived Signals for Heart Rate and Variability Estimation in In-Vehicle Applications: A Comparative Review and Empirical Validation

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Signal Quality Assessment and Reconstruction of PPG-Derived Signals for Heart Rate and Variability Estimation in In-Vehicle Applications: A Comparative Review and Empirical Validation

Gao Ruimin, Carl Miller, Brian T W Lin, Chris Schwarz and Monica L H Jones

Sensors (Basel, Switzerland), Vol.25(24), 7556

12/02/2025

DOI: 10.3390/s25247556

PMCID: PMC12736534

PMID: 41471550

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Abstract

Electrocardiography (ECG) is widely recognized as the gold standard for measuring heart rate (HR) and heart rate variability (HRV). However, photoplethysmography (PPG) presents notable advantages in terms of wearability, affordability, and ease of integration into consumer devices, despite its susceptibility to motion artifacts and the absence of standardized processing protocols. In this study, we review current ECG and PPG signal processing methods and propose a signal quality assessment and reconstruction pipeline tailored for dynamic, in-vehicle environments. This pipeline was evaluated using data gathered from participants riding in an automated vehicle. Our findings demonstrate that while blood volume pulse (BVP) derived from PPG can provide reliable heart rate estimates and support extraction of certain HRV features, its utility in accurately capturing high-frequency HRV components remains constrained due to motion-induced noise and signal distortion. These results underscore the need for caution in interpreting PPG-derived HRV, particularly in mobile or ecologically valid contexts, and highlight the importance of establishing best practices and robust preprocessing methods to enhance the reliability of PPG sensing for field-based physiological monitoring.

Algorithms

Machine Learning

Nervous System

Physiology

Signal Processing

Cardiac arrhythmia

Classification

Datasets

Electrocardiography

Entropy

Fatigue

Heart rate

Light

Neural networks

Performance evaluation

Sensors

Skin

Details

Title: Subtitle: Signal Quality Assessment and Reconstruction of PPG-Derived Signals for Heart Rate and Variability Estimation in In-Vehicle Applications: A Comparative Review and Empirical Validation
Creators: Gao Ruimin
Carl Miller - Michigan Department of Transportation
Brian T W Lin
Chris Schwarz - University of Iowa
Monica L H Jones
Resource Type: Journal article
Publication Details: Sensors (Basel, Switzerland), Vol.25(24), 7556
DOI: 10.3390/s25247556
PMID: 41471550
PMCID: PMC12736534
NLM abbreviation: Sensors (Basel)
ISSN: 1424-8220
eISSN: 1424-8220
Publisher: MDPI AG
Grant note: Ford-University of Michigan Alliance
This project was funded by the Ford-University of Michigan Alliance.
Language: English
Date published: 12/02/2025
Academic Unit: The National Advanced Driving Simulator; Driving Safety Research Institute
Record Identifier: 9985093882102771

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