Study of Activity Tracking through Bluetooth Low Energy-Based Network
Bibliographic record
Abstract
This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".