Digital Twin Factory : Center for High Performance Systems : Texas State University

Real-Time Industrial Avatars

The digital twins of manufacturing and material handling operators featuring real-time bio-feedback to prevent work-related injuries.

Toyota Material Handling North America (TMHNA) Funded Project

Our project received funding through the TMHNA University Research Program to create digital twin representations of material handling operators.

Digital Twin Applications in Material Handling Operations

The prestigious MHI Solutions Magazine published our concept article of the "digital twin," which discusses the opportunities and challenges for the application of this technology in the material handling industry.

AR-Enabled Technology

The digital twin framework produces an Augmented Reality (AR) holographic representation of the material handling operator, which assists in the delivery of real-time bio-feedback.

Publications

Improving the material handling industry workforce through digital representations of human-based operations
by Jesus A. Jimenez

This article presents the opportunities and challenges of digital twin applications for manual material handling systems.

A motion capture system framework for the study of human manufacturing repetitive motions
by Francis Mendez; David Wierschem; Jesus A. Jimenez

This seminal paper proposes a framework to obtain and analyze real-time data concerning the dynamic and natural motion of individuals in manufacturing-like processes that involve human labor. The framework consists of a tracking system, a system of sensors, a processor that collects time-series data, data processing, and an alert system.

A digital twin framework of a material handling operator in industry 4.0 environments
by Abhimanyu Sharotry; Jesus A. Jimenez; David Wierschem; Francis Mendez; George Koutitas; Damian Valles; Semih Aslan; Rachel R. Koldenhoven

This paper presents a framework of a manual material handling operator in order to enhance worker performance by using a digital twin approach. The framework is composed of three modules: (1) Data Collection Module, (2) Digital Twin Application Module, (3) Operator Analysis/Feedback Module.

Machine learning techniques for motion analysis of fatigue from manual material handling operations using 3D motion capture data
by Geovanni Hernandez; Damian Valles; David C. Wierschem; Rachel M. Koldenhoven; George Koutitas; Francis A. Mendez; Semih Aslan; Jesus A. Jimenez

This paper evaluates machine learning techniques, based on Recurrent Neural Networks (RNN), to evaluate the fatigue factor caused by repetitive motions.