Global Embodiment Index: Toward Objective Embodiment Metrics for Upper-limb Prosthetic Training in eXtended Reality
Bibliographic record
Abstract
This study proposes the Global Embodiment Index (GEI) as a quantitative and objective metric for assessing user embodiment during upper-limb prosthesis training in XR environments. Current methods for measuring embodiment often rely on subjective evaluations or capture only isolated aspects of the embodiment experience. GEI addresses these limitations by integrating three core dimensions: motor-control naturalness, sensorimotor integration, and task efficiency and effort. Each dimension is evaluated using dedicated sensors and parameters selected to reflect human factors known to influence embodiment. These dimensions are then combined into a single, interpretable score through harmonic mean aggregation and logistic mapping.To evaluate the computational validity of GEI, we conducted a Monte Carlo simulation using synthetic user data to compare embodiment profiles between body-powered and EMG-based prosthetic devices. The results demonstrate that GEI sensitively captures key trade-offs among control fidelity, sensory feedback, and task efficiency, revealing clear performance distinctions between device types. GEI holds promise for a wide range of applications, including optimization of XR-based training, device benchmarking and selection, adaptive feedback control, and long-term rehabilitation monitoring.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".