Speech Recognition Driven Assistive Framework for Remote Patient Monitoring
Bibliographic record
Abstract
Health care resources have started to become scarce due to their increase in demand. Hospitals have begun to run out of space, forcing them to deny admission of patients. Remote Patient Monitoring (RPM) has the potential to help citizens who suffer from chronic diseases and provide environments were easy to access healthcare is available. RPM allows people to receive the same amount of care without having to difficulties to find a spot at a hospital ward. However, some roadblocks end up preventing RPM from being implemented by more healthcare providers. Data integrity, user privacy, and high power consumption are some of these concerns. With data transmission and transaction, privacy and confidentiality have always been an issue. High power consumption is a concern due to RPM's demand for continuous data collection. This paper proposes a framework that reinforces the RPM system to address these concerns. The design not only allows better data filtering for privacy but also a more responsive system with the use of controlled surveillance and speech recognition. Overall, this framework provides an opportunity for RPMs to be a viable implementation for healthcare providers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".