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.
bio-signal fusion, computer vision, digital health, digital twin, machine learning, metaverse
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
Link to MDPI article: https://www.mdpi.com/1424-8220/22/24/9747
OA article, made available by MDPI
License: CC BY 4.0
Uploaded: 15 Feb. 2023