Smart environments using near-field communication and HTML5
Bibliographic record
Abstract
Home health care and home automation increasingly allows more seniors to maintain independence, and remain longer in their own homes. Similarly, a post-surgical patient may be discharged from a medical facility to their house, which electronically facilitates their recuperation and promotes recovery. Smart environments are making the task of providing assistive technology in the home easier and more affordable. Near-field communication (NFC) has become popular in recent years. Increasing uptake of NFC-enabled smartphones has opened a new avenue to facilitate creation of a smart environment without the need for significant infrastructure. HTML5 is the latest version of the hypertext markup language, with unique code that enables access to advanced features on a smartphone. Proprietary apps can potentially be inconvenient and inconsistent and may even decrease uptake of the technology. In this paper, we propose a new methodology to enable NFC tags and NFC smartphones in conjunction with HTML5 backbone code, to be used for smart environments in home health care applications without the need for specific applications to be installed on the smartphone. Results show significant promise with just the built in phone software with use of NFC and HTML5 for various applications of smart environments. In many common tasks in a smart environment that increase patient safety, NFC tags can be not only informative, but an integral component of the system by triggering specific HTML5 code to provide appropriate responses - without the need to install specialized apps as long as the NFC is enabled in the mobile device.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".