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Improving real-time physiological signs estimation using plethysmography wave and heterogeneous embedded system.

Zakaria El Khadiri¹, Rachid Latif¹, Amine Saddik¹

¹Laboratory of Systems Engineering and Information Technology LISTI, National School of Applied Sciences, Ibn Zohr University Agadir, Agadir, Morocco.

Journal of Medical Engineering & Technology

|February 17, 2025

Summary

This study introduces an efficient embedded system for real-time physiological monitoring using camera-based Photoplethysmography (PPG) signals. The optimized algorithm significantly reduces processing time for vital signs estimation, enhancing embedded system performance.

Keywords:

Adaptive Dual threshold Filter CPU-GPU Photoplethysmography signal Variational Mode decomposition parallel computing

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Area of Science:

Biomedical Engineering
Computer Science

Background:

Physiological monitoring is crucial for healthcare.
Current methods often require specialized hardware.
Camera-based Photoplethysmography (PPG) offers a non-contact alternative.

Purpose of the Study:

To develop and implement a real-time embedded system for vital signs monitoring (heart and breathing rates).
To evaluate the processing time and algorithmic complexity of the proposed approach.
To assess the accuracy and hardware-software adoption of the embedded implementation.

Main Methods:

Utilized image processing to extract PPG signals from RGB cameras.
Applied signal processing, filtering, and decomposition algorithms for vital signs estimation.
Implemented the algorithm using High-Level Synthesis (HLS) on embedded platforms (CPU/GPU).

Main Results:

Achieved significant processing time optimization compared to native MATLAB and optimized C/C++ versions.
Demonstrated gains of x5.05, x24.96, and x36.68 using different optimization tools (OpenMP, OpenCL).
Validated the hardware-software co-design for efficient embedded adoption.

Conclusions:

The proposed real-time embedded implementation enables accurate and efficient physiological monitoring.
The optimized algorithm significantly reduces computational load, suitable for resource-constrained embedded systems.
Camera-based PPG monitoring with embedded processing presents a promising non-contact vital signs assessment solution.