A Vital-Signs Monitoring Wristband With Real-Time In-Sensor Data Analysis Using Very Low-Hardware Resources
Bibliographic record
Abstract
We present a cost-effective multi-sensor wristband that monitors vital signs, including heart rate, respiratory rate, and oxygen saturation, with very low-hardware resources, while effectively mitigating motion artifacts. Our system, which operates in real-time, optimizes resource utilization and minimizes memory footprint by intelligently managing motion artifacts during vital signs extraction. For this purpose, we introduce a newIntelligent Domain Fusionalgorithm, which allows to reduce resource allocation during moderate motion artifacts by up to a 30% in memory usage, while achieving an average latency of as short as 18 ms in worst case. To further enhance efficiency, our frequency domain analysis is optimized through the utilization of a Z-chirp transform, minimizing memory utilization during Fourier transforms. Our prototype is built around a real-time operating system (freeRTOS) running on an ARM-M4 architecture, enabling Bluetooth Low Energy (BLE) 5.0 control, data processing, and communication. It incorporates an onboard 1-Gbit NAND flash memory for continuous data storage, when no wireless connection is available, offering up to 7 hours of uninterrupted sensor data recording. The sensor data collected by our prototype encompasses a comprehensive range of raw and processed information critical for monitoring various physiological and environmental parameters in a concise message format comprising a data ID, a data payload, and a timestamp. With a small 250 mAh battery, our prototype provides an autonomy of 18 hours of continuous data collection, averaging a mere 10.41 mAh of power consumption.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".