Bibliographic record
Abstract
The human digital twin (HDT) represents a comprehensive digital counterpart of an individual, integrating biophysical, cognitive, psychological, and social attributes. The concept of HDT comes from the conventional digital twin used in industrial engineering. Its application extends into personalized healthcare, smart manufacturing, and human-centered systems. This paper reviews the evolution of human modelling, from early physical representations to modern digital simulations, leading to the development of HDT. A key focus is unified HDT information modelling, consolidating diverse human attributes into an integrated information model. The paper categorizes essential HDT components, including physical, mental, psychological, ability, activity, and social-connection models. Additionally, This paper highlights challenges such as data and software standardization, privacy concerns, technical limitations, and the complexity of modelling dynamic human characteristics. Addressing these challenges will be crucial for advancing HDT applications in industry and healthcare. Future research directions include exploring data collection and simulation methods to empower the HDT functions in real-world applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".