Towards human digital twin: Reviewing human modelling and simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The human digital twin (HDT) is a detailed and personalized digital representation of an individual, encompassing the physical, cognitive, psychological, and social characteristics. HDT, an extension of the traditional digital twin concept from the industrial engineering sector, finds applications in diverse human-centric sectors such as smart manufacturing, medical healthcare, personal fitness, and autonomous driving. Although human modelling and simulation (HMS) are essential for advancing HDT technology, existing literature reviews primarily emphasize general aspects, including the definition, hierarchical frameworks, and various applications of HDT, rather than providing a thorough overview of HMS methods and tools. To fill the gap, this review work is specifically focused on the HMS aspect in HDT, discussing the evolution of digital human simulation, HDT information models, HDT metamodels, and related tools and software. This study also provides a checklist on building the HDT metamodel from the collected human data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it