The Role and Challenges of Body Channel Communication in Wearable Flexible Electronics
Bibliographic record
Abstract
Flexible electronics are compatible with film substrates that are soft and stretchable, resulting in conformal integration with human body. Integrated with various sensors and communication ICs, wearable flexible electronics are able to effectively track human vital signs without affecting the body activities. Such a wearable flexible system contains a sensor, a front-end amplifier (FEA), an analog-to-digital converter (ADC), a micro-controller unit (MCU), a radio, a power management unit (PMU), where the radio is the design bottleneck due to its high power consumption. Different from conventional wireless communications, body channel communication (BCC) uses the human body surface as the signal transmission medium resulting in less signal loss and low power consumption. However, there are some design challenges in BCC, including body channel model, backward loss, variable contact impedance, stringent spectral mask, crystalless design, body antenna effect, etc. In this paper, we conduct a survey on BCC transceiver, and analyze its potential role and challenges in wearable flexible electronics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".