Tattoo Assisted Optical Sensor System for Multimodal Discrete Physiological Sensing
Bibliographic record
Abstract
This work introduces a novel method for recording electrophysiological signals and sensing physiological events using an optical tattoo sensor (OTS), eliminating the need for integrating electronics on the skin. Traditional sensors face challenges due to the mechanical mismatch between rigid silicon-based circuits and soft, stretchable sensors, leading to poor performance and interface failures. The OTS, which can be applied as a conventional temporary tattoo, eliminates scattered light from beneath the skin. When used with a handheld speckle sensing device, it improves signal-to-noise ratio and stability in capturing physiological activities beneath the skin. Using the tattoo assisted portable optical sensing system, different types of electrophysiological signal recording and physiological events sensing, including electrocardiography (ECG), electromyography (EMG), seismocardiography (SCG), respiration rate, and pulses, are performed. A shallow neural network is developed to convert the detected skin motions into electrophysiological signals such as ECG. The electrophysiological recording using OTS shows consistency with electrically measured signals.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".