Remote Health Monitoring System for Bedbound Patients
Bibliographic record
Abstract
Abstract There are many patients who require continuous monitoring of vital signs and their sleep position such as bedbound patients and hospitalized patients. Also, in some cases, like COVID-19, it is critical for a caregiver to keep a safe distance to the patient. For remote monitoring, radar technologies have been shown to be promising. Thus, in this paper, we present a novel solution for the remote breath and sleep position monitoring by using a multi-input-multi-output (MIMO) radar. Our proposed system could monitor a number of people simultaneously, and therein we use a high-resolution direction of arrival (DOA) detection for finding close targets. Furthermore, the sleep position of each target is determined using a support vector machine (SVM) classifier. The breath analysis involves designing an optimum filter for estimating both the breathing rate and the noiseless breathing waveform. Furthermore, we tested the system by hand-made targets and real human targets. The radar placed in a bedroom environment above a bed where two subjects were sleeping next to each other. For the breathing rate, the accuracy of the radar is more than 97% for human subjects compared with a reference sensor. Also, the sleep position correct detection is more than 83%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".