Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Healthcare Technology

Background:

• Remote photoplethysmography (rPPG) offers non-contact, continuous heart rate (HR) monitoring.
• Practical rPPG applications face challenges with irregular sampling rates and data loss, impacting HR estimation accuracy.
• Existing HR estimation methods often assume fixed sampling intervals, limiting real-world applicability.

Purpose of the Study:

• To develop and evaluate low-complexity timing correction methods for improving HR estimation from rPPG signals.
• To address inaccuracies caused by irregular sampling and missing data in rPPG measurements.
• To identify efficient timing correction techniques suitable for edge-computing healthcare applications.

Main Methods:

• Implementation of linear, cubic, and filter interpolation for timing correction.
• Comparative analysis of different interpolation methods for HR estimation accuracy.
• Evaluation of computational complexity for edge-computing suitability.

Main Results:

• Low-complexity timing correction methods significantly improve HR estimation reliability from rPPG signals.
• Cubic interpolation offers robust signal reconstruction but demands higher computational resources.
• Linear and filter interpolation provide efficient alternatives for HR estimation under irregular sampling.

Conclusions:

• Timing correction is crucial for accurate rPPG-based HR estimation in real-world scenarios.
• The proposed methods enhance the robustness of non-contact HR monitoring for healthcare.
• Efficient interpolation techniques enable practical edge-computing applications for remote health monitoring.