Exploring a Mixed Reality Framework for the Internet-of-Things: Toward Visualization and Interaction with Hybrid Objects and Avatars
Bibliographic record
Abstract
Smart hyper-connected environments are becoming a central part of daily life in modern society. Such environments apply the internet-of-things (IoT) paradigm [1], which refers to the growing field of interconnected devices and the networking that supports smart, embedded applications. The IoT has many human-computer interaction (HCI) challenges [2], however, and central to these challenges is the need to provide more human-friendly approaches to communicating sensor information and meaningful visualizations of contextual states to users of IoT systems. Highly expressive, and engaging smart environment interfaces are uncommon, and this work applies mixed reality as a tool to better visualize and express the underlying behaviors and states within IoT smart devices. This extends the authors' previous research [3], providing a new head-mounted display framework and interconnection architecture for an augmented reality representation of a physical IoT device, an IoT Avatar. The video submission demonstrates contributions for: i) an exploration of how mixed reality can be used to enrich smart spaces and hybrid objects, and ii) an early use case and functionality evaluation of a simple avatar hybrid smart object that expresses emotion through immersive media. It is expected that this research will help foster immersive and engaging human-centered interaction in future smart environments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".