Remote Health Monitoring System for Bedbound Patients
Bibliographic record
Abstract
In this paper, we present a novel solution for the remote breathing and sleep position monitoring by using a multi-input-multi-output (MIMO) radar. Our proposed system is able to monitor a number of people simultaneously, and therein we use a high-resolution direction of arrival (DOA) detection for finding closely separated targets. So, it effectively increases the number of target detection and reduces the cost by reducing the number of sensors. Furthermore, our proposed system is capable of identifying the sleep position of each monitored person by selecting appropriate target features and using a support vector machine (SVM) classifier. The breathing analysis involves designing an optimum filter for estimating both the breathing rate and the noiseless breathing waveform. In addition, we use the radar in a bedroom environment above a bed where two subjects sleep next to each other. The accuracy of the breathing monitoring subsystem is more than 97% for human subjects in the bedroom compared with a reference sensor. Also, the correct rate for sleep position detection is more than 83%.
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 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".