A Telemedicine System for Remote Health and Activity Monitoring for the Elderly
Bibliographic record
Abstract
The aging population is placing increasing pressure on healthcare services around the world. Telemedicine, which provides medical information or healthcare services at a distance using telecommunication technologies, is of growing interest to governments and healthcare providers. Existing telemedicine systems are primarily for medical information sharing and consultation with no teleoperation capabilities for activity monitoring. Moreover, the equipment of most systems available to support older patients to stay in their living environment must be tied to a fixed location, which severely limits their feasibility and applicability. In this paper, a new telemedicine structure is introduced for regular and ad hoc health monitoring services. In particular, it aims at scenarios where frequent interactive contacts between patients and professionals are required. This system incorporates several different networking technologies that work harmoniously to facilitate data communication, which potentially have a profound impact on the method of delivering medical service remotely. Another unique characteristic of the developed system is its capabilities of adaptation to network conditions, such as network congestion and availability of bandwidth. The concept of the proposed structure is validated using a laboratory-based test platform with some pilot experiments. Preliminary results demonstrate its feasibility for remote health monitoring services of the elderly. The potential benefits of the system are also presented.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".