Towards Human-Centric Smart Manufacturing: A Digital Twin Enabled Affective Ergonomic Framework For Adaptive Human Robot Collaboration

Waleed khan; Muhammad Danish Rasheed; Muhammad Akram; Muhammad Imran; Rana Hassam Ahmed; Abdul Rauf

doi:10.29284/5j105z61

Authors

Waleed khan College of dupage, 425 Fawell Blvd, Glen Ellyn, IL 60137, United States
Muhammad Danish Rasheed Department of Information Technology, Berkeley City College, Berkeley, United States of America.
Muhammad Akram Harper community college ,1200 Algonquin Rd, Palatine, IL 60067, United States
Muhammad Imran Washington University of Science and Technology, Department of Information Technology, Artificial Intelligence, Cyber Security
Rana Hassam Ahmed Department of Computer Science, The University of Faisalabad, Faisalabad, Punjab, Pakistan
Abdul Rauf Department of Computer Science, The University of Faisalabad, Faisalabad, Punjab, Pakistan

DOI:

https://doi.org/10.29284/5j105z61

Keywords:

Human-Robot Collaboration, Digital Twin, Affective Computing, Ergonomics, Smart Manufacturing, Adaptive Control, Human-Centric AI.

Abstract

The advent of Industry 4.0 has ushered in an era of Smart Manufacturing, where Human-Robot Collaboration (HRC) is pivotal for enhancing productivity and flexibility. However, existing HRC paradigms often overlook the dynamic internal states of human operators, focusing primarily on task efficiency and physical safety. This oversight can lead to suboptimal performance, increased stress, ergonomic risks, and reduced job satisfaction. This paper proposes a novel Digital Twin (DT) enabled Affective-Ergonomic Framework designed to foster truly human-centric adaptive HRC. Our framework integrates real-time multi-modal sensing to continuously monitor human operators' affective (e.g., stress, fatigue) and ergonomic (e.g., posture, physical load) states. These data feed into a sophisticated Human Digital Twin (HDT), which leverages machine learning models to infer and predict human states. An adaptive decision-making engine then utilizes this HDT data, alongside Robot and Environment Digital Twins, to dynamically adjust robot behavior (e.g., speed, task allocation, assistance level) in real-time. We present the architectural design, illustrative mathematical models for state inference, and a conceptual case study demonstrating the framework's potential to significantly improve human well-being, mitigate ergonomic risks, enhance safety, and ultimately boost overall system performance in smart manufacturing environments. This approach paves the way for intelligent HRC systems that proactively respond to human needs,

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