Bluetooth Low Energy Based Activity Tracking of Patient
Bibliographic record
Abstract
With growing demand for health care, medical clinicians and researchers need to gather quality patient centric data in order to better develop tools to improve quality of life. As an example, it has been a challenge to accurately evaluate postoperative mobility in patient care units. This paper describes the development and implementation of a proof-of-concept, a low cost and easily deployable Bluetooth Low Energy (BLE) based localization system used to identify and locate patients. Using the received signal strength (RSS) of BLE signal and the uniqueness of BLE hardware addresses, patients can be identified and located within the hospital room. The value of RSS of BLE signal from wearable BLE beacon varies with distance to the wall anchored BLE scanner. The first part of the paper presents the results of the experiments conducted in a low noise and non-reflective environment. The second half of the paper talks about the proposed algorithm of localization of the subject within the experimental room. The paper evaluates the effects of the number of BLE wall anchored scanners and the number of packets on the accuracy of the proposed algorithm. The proposed algorithm was evaluated to have better performance than other classical approaches.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".