An RF-based wearable sensor system for indoor tracking to facilitate efficient healthcare management
Bibliographic record
Abstract
To understand the utilization of clinical resources and improve the efficiency of healthcare, it is often necessary to accurately locate patients and doctors in a healthcare facility. However, existing tracking methods, such as GPS, Wi-Fi and RFID, have technological drawbacks or impose significant costs, thus limiting their applications in many clinical environments, especially those with indoor enclosures. This paper proposes a low-cost and flexible tracking system that is well suited for operating in an indoor environment. Based on readily available RF transceivers and microcontrollers, our wearable sensor system can facilitate locating users (e.g., patients or doctors) or objects (e.g., medical devices) in a building. The strategic construction of the sensor system, along with a suitably designed tracking algorithm, together provide for reliability and dispatch in localization performance. For demonstration purposes, several simplified experiments, with different configurations of the system, are implemented in two testing rooms to assess the baseline performance. From the obtained results, our system exhibits immense promise in acquiring a user location and corresponding time-stamp, with high accuracy and rapid response. This capability is conducive to both short- and long-term data analytics, which are crucial for improving healthcare management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".