Mechanically Flexible Wireless Multisensor Platform for Human Physical Activity and Vitals Monitoring
Bibliographic record
Abstract
Practical usability of the majority of current wearable body sensor systems for multiple parameter physiological signal acquisition is limited by the multiple physical connections between sensors and the data-acquisition modules. In order to improve the user comfort and enable the use of these types of systems on active mobile subjects, we propose a wireless body sensor system that incorporates multiple sensors on a single node. This multisensor node includes signal acquisition, processing, and wireless data transmission fitted on multiple layers of a thin flexible substrate with a very small footprint. Considerations for design include size, form factor, reliable body attachment, good signal coupling, low power consumption, and user convenience. The prototype device measures 55 15 mm and is 3 mm thick. The unit is attached to the patient's chest, and is capable of performing simultaneous measurements of parameters, such as body motion, activity intensity, tilt, respiration, cardiac vibration, cardiac potential (ECG), heart rate, and body surface temperature. In this paper, we discuss the architecture of this system, including the multisensor hardware, the firmware, a mobile-phone receiver unit, and assembly of the first proof-of-concept prototype. Preliminary performance results on key elements of the system, such as power consumption, wireless range, algorithm efficiency, ECG signal quality for heart-rate calculations, as well as synchronous ECG and body activity signals are also presented.
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 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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".