Extended-XRI Body Interfaces for Hyper-Connected Metaverse Environments
Bibliographic record
Abstract
Hybrid mixed-reality (XR) internet-of-things (IoT) research, here called XRI, aims at a strong integration between physical and virtual objects, environments, and agents wherein IoT -enabled edge devices are deployed for sensing, context understanding, networked communication and control of device actuators. Likewise, as augmented reality systems provide an im-mersive overlay on the environments, and virtual reality provides fully immersive environments, the merger of these domains leads to immersive smart spaces that are hyper-connected, adaptive and dynamic components that anchor the metaverse to real-world constructs. Enabling the human-in-the-loop to remain engaged and connected across these virtual-physical hybrid environments requires advances in user interaction that are multi-dimensional. This work investigates the potential to transition the user in-terface to the human body as an extended-reality avatar with hybrid extended-body interfaces that can interact both with the physical and virtual sides of the metaverse. It contributes: i) an overview of metaverses, XRI, and avatarization concepts, ii) a taxonomy landscape for extended XRI body interfaces, iii) an architecture and potential interactions for XRI body designs, iv) a prototype XRI body implementation based on the architecture, v) a design-science evaluation, toward enabling future design research directions.
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 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 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".