Bibliographic record
Abstract
The proliferation of Information Technology (IT) in the healthcare domain enables healthcare institutions to deliver healthcare services to a wider audience in an efficient manner.Currently, patients can be monitored and treated remotely on time with high standards of care; caregivers can communicate together to achieve more timely and effective performance for the entire care process; assets which are scattered throughout the hospitals can be managed and utilized efficiently.These stakeholders: patients, caregivers and assets are mobile in nature and they are commonly tagged with sensory badges that can communicate either using the existing wireless infrastructure or using its own infrastructure.Hence, the term medical sensory system can be used to refer to the network of sensors that represent the stakeholders as mobile objects along with the backhaul infrastructure.Thereby, this network can be viewed as a Wireless Sensor Network (WSN) which has its own characteristics that result in some challenges.Among these challenges is the process of localizing objects, which is complicated due to the ad hoc deployment manner of a WSN, as well as the constraints on sensors in terms of cost, size, and energy consumption.Localization and tracking systems are those concerned with localizing multiple objects and keeping track of their locations over time to enhance services in different industries including healthcare.Currently, there are localization and tracking systems that have been proposed in the literature to address the localization problem; some of them are directed to the healthcare domain, while others are proposed for general usage.This paper discusses the healthcare domain stakeholders' requirements, presents different localization and tracking systems in the literature and provides qualitative analysis and critique of each system based on such requirements.The paper also touches on a few of the commercial systems in the industry.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".