Digital Twin for Amputees: A Bidirectional Interaction Modeling and Prototype with Convolutional Neural Network

Abstract

With over 2 million amputees in the U.S., they have been facing significant employment challenges. However, several physical prostheses still lack usability to be able for them to work in person. Therefore, this study explores an innovative application of digital twin approach, focusing on bidirectional interaction modeling and prototyping using convolutional neural networks (CNNs). We developed a simplified digital twin environment integrating electromyography (EMG) sensors and virtual reality (VR) to enable real-time interaction between the virtual and physical worlds. The CNN model, trained to classify hand movement from EMG data, achieved a test accuracy of 99%, demonstrating its effectiveness for practical applications. Our framework facilitates remote control of physical devices through VR gestures, potentially allowing amputees to perform meaningful work from home, thus overcoming physical limitations and fostering greater independence. This preliminary study underscores the possibility of digital twin technology to redefine workplace accessibility, offering amputees opportunities for potential employment.