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Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion.

Izaldein Al-Zyoud¹, Fedwa Laamarti^1,2, Xiaocong Ma¹

¹Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This study introduces a digital twin system to fuse heart rate, breathing rate, and blood oxygen saturation using computer vision and machine learning. The system accurately models and measures these vital signs for advanced digital health applications.

Keywords:

bio-signal fusion computer vision digital health digital twin machine learning metaverse

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Area of Science:

Biomedical Engineering
Digital Health
Computer Vision

Background:

Advancing human bio-signal fusion is crucial for secure digital health and metaverse applications.
Current methods require improvement for comprehensive well-being monitoring.

Purpose of the Study:

To propose a data-driven digital twin (DT) system for fusing three key human bio-signals.
To enable secure and modern digital health applications.

Main Methods:

Utilized computer vision with non-invasive photoplethysmography (PPG) to extract bio-signals from facial videos.
Implemented machine learning (ML) to model and measure heart rate (HR), breathing rate (BR), and blood oxygen saturation (SpO2).

Main Results:

Successfully demonstrated the digital twin's capability in modeling and measuring HR, BR, and SpO2.
Achieved strong performance and accuracy compared to ground-truth values.

Conclusions:

The developed system provides a foundation for a holistic human health and well-being DT model.
This research paves the way for real-world medical applications in digital health.