Network of wireless medical devices to assess activity using non-traditional sensors
Bibliographic record
Abstract
In this paper, we present the design of two smart sensors that were developed for deployment within a body worn wireless network of medical devices. Ambulatory and remote monitoring of biomedical signals have gained importance in the last decade as medical institutions try to reduce costs by discharging patients earlier while still requiring various levels of monitoring. Most of the systems currently on the market are bulky, closed architecture, static in configuration and use wired medical devices, all of which limit their usage. Most current devices provide only raw signals that are processed by a central-wearable computer limiting scalability. Queens University and the Royal Military College of Canada have teamed up to research and design a new generation of ambulatory medical telemetry systems. The aim of the research is to develop an open architecture using Real-Time Object Oriented Modeling that will allow wireless, wearable medical devices to join a dynamically configurable monitoring environment. The intent of the system is to monitor patients recovery by measuring biometrics and biomedical signals as they go about their daily activities in the comfort of their homes. The sensors that are being developed as part of this research are smart sensors that can provide pre-processed information, reducing the load on the wearable computer.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".