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 new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intelligent Domain Fusion</i> algorithm, 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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".