Towards Human-Centric Smart Manufacturing: A Digital Twin Enabled Affective Ergonomic Framework For Adaptive Human Robot Collaboration
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".