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

Authors

Izaldein Al-Zyoud, Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Canada
Fedwa Laamarti, Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Canada & Mohamed bin Zayed University of Artificial IntelligenceFollow
Xiaocong Ma, Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Canada
Diana Tobón, Faculty of Engineering, University of Medellín, Medellín, Colombia
Abdulmotaleb Elsaddik, Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Canada & Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

Sensors

Abstract

Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications. © 2022 by the authors.

DOI

10.3390/s22249747

Publication Date

12-12-2022

Keywords

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

Comments

OA article, made available by MDPI

License: CC BY 4.0

Uploaded: 15 Feb. 2023

Recommended Citation

I. Al-Zyoud, F. Laamarti, X. Ma, D. Tobón, and A. El Saddik, “Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion,” Sensors, vol. 22, no. 24, p. 9747, Dec. 2022, doi: 10.3390/s22249747

Additional Links

Link to MDPI article: https://www.mdpi.com/1424-8220/22/24/9747