On-Body e-Hub: Accessibility Technology for People with Disabilities
Bibliographic record
Abstract
This paper presents an innovative approach to adapting the Next Generation First Responder (NGFR) communication platform for two critical applications: day-to-day care of individuals with disabilities and disaster management scenarios. The NGFR system, currently under Research and Development (R&D) by the US Department of Homeland Security, shows promising potential for extension beyond its original scope. This study outlines three key R&D adaptation paths: a new taxonomical view of assistive technologies; architecture modification of the existing NGFR framework to accommodate new use cases; and elicitation process protocols for gathering user requirements and preferences. This adaptation incorporates compact, energy-efficient, and cost-effective devices in wearable format for seamless integration. These devices provide comprehensive on-body sensing capabilities for physiological metrics monitoring as well as environmental condition assessment, supported by a robust infrastructure that includes cloud computing and other external resources. This research provides a roadmap for improving assistive technologies for individuals with disabilities, focusing on optimal selection of tools tailored to individual needs. The optimization is achieved by using a unique expert elicitation mechanism known as the reference protocol, which is proposed as a crucial step toward the practical design of customized on-body e-hubs. The proposed roadmap is directed at both technology developers and users, as well as at care providers, including emergency responders, social workers, and healthcare professionals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".