Real-Time Human Synchronization Framework for Digital Twin

EasyChair Preprint 10533

Abstract

The concept of digital twin, which synchronizes data from physical objects with the virtual world, has been widely used in various fields. However, previous studies have focused on synchronizing robots due to their built-in sensors, while synchronizing humans is more complex due to their high degree of freedom and the need for external sensors. Although studies have been conducted to synchronize humans using cameras, they have only considered a single space. To apply this technology in real buildings or factories, a large-scale digital twin using multiple sensors is required. To address this issue, we propose a framework that synchronizes large-scale digital twins using only the features of people in images, rather than raw data from RGB camera sensors, reducing network traffic. This framework allows for the storage and replay of only the synchronized human's features, facilitating interaction with other robots and customization.

Keyphrases: Digital Twin, IoT, synchronization

@booklet{EasyChair:10533,
  author    = {Donghoon Lee and Joongho Cho and Jaeho Kim},
  title     = {Real-Time Human Synchronization Framework for Digital Twin},
  howpublished = {EasyChair Preprint 10533},
  year      = {EasyChair, 2023}}