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

Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model

Digital Twin (DT), a concept of Healthcare (4.0), represents the subject's biological properties and characteristics in a digital model. DT can help in monitoring respiratory failures, enabling timely interventions, personalized treatment plans to improve healthcare, and decision-support for healthcare professionals. Large-scale implementation of DT technology requires extensive patient data for accurate monitoring and decision-making with Machine Learning (ML) and Deep Learning (DL). Initial respiration data was collected unobtrusively with the ESP32 Wi-Fi Channel State Information (CSI) sensor. Due to limited respiration data availability, the paper proposes a novel statistical time series data augmentation method for generating larger synthetic respiration data. To ensure accuracy and validity in the augmentation method, correlation methods (Pearson, Spearman, and Kendall) are implemented to provide a comparative analysis of experimental and synthetic datasets. Data processing methodologies of denoising (smoothing and filtering) and dimensionality reduction with Principal Component Analysis (PCA) are implemented to estimate a patient's Breaths Per Minute (BPM) from raw respiration sensor data and the synthetic version. The methodology provided the BPM estimation accuracy of 92.3% from raw respiration data. It was observed that out of 27 supervised classifications with k-fold cross-validation, the Bagged Tree ensemble algorithm provided the best ML-supervised classification. In the case of binary-class and multi-class, the Bagged Tree ensemble showed accuracies of 89.2% and 83.7% respectively with combined real and synthetic respiration dataset with the larger synthetic dataset. Overall, this provides a blueprint of methodologies for the development of the respiration DT model.

Digital Twins for Building Industrial Metaverse

INTRODUCTION

The concept of the metaverse, a virtual world where users can interact with a computer-generated environment, has received significant attention recently.

OBJECTIVES

This study aims to investigate the application and fundamental technologies of Digital Twins (DT) in the development of the industrial metaverse, to enhance factory production efficiency.

METHODS

The study adopts a literature review approach to explore the architecture and key technologies of the industrial metaverse, including DT, cloud rendering, virtual-real interaction, big data visualization, and the Internet of Things. Firstly, this study analyzes the architecture and key technologies of the Industrial Metaverse, which encompass DT, cloud rendering, virtual-reality interaction, big data visualization, and the Internet of Things (IoT). These technologies are essential components in enabling the transformation from traditional manufacturing paradigms to intelligent manufacturing within the industrial metaverse. Secondly, this study investigates the key technological aspects that support DT in the industrial metaverse. It highlights the crucial role played by DT in facilitating the advancement and realization of intelligent manufacturing.

RESULTS

The study finds that DT plays a crucial role in the industrial metaverse, enabling the creation of virtual replicas of physical assets that can be monitored and analyzed in real-time.

CONCLUSION

The development of the industrial metaverse holds significant potential for the industrial sector, providing a platform for optimizing and improving manufacturing processes.