In‐Ear Electronics with Mechanical Adaptability for Physiological Sensing
Bibliographic record
Abstract
Significant developments have been made in the field of wearable healthcare by utilizing soft materials for the construction of electronic sensors. However, the lack of adaptability to complex topologies, such as ear canal, results in inadequate sensing performance. Here, we report an in-ear physiological sensor with mechanical adaptability, which softens upon contact with the ear canal's skin, thus reducing the sensor-skin mechanical mismatch and interface impedance. An efficient strategy of mechanical adjustment and switching is exploited to increase the softness of the device, leading to a significant decrease in Young's modulus from 30.5 MPa of thermoplastic polyurethane (TPU) to 0.86 MPa of TPU/Ecoflex foam (TEF).The mechanical adaptability at body temperature endows the in-ear device improved device-canal contact area and interface stability. As a result, the TEF-based in-ear device demonstrates reliable sensing, low motion artifact, and high comfort in electroencephalography (EEG) and core body temperature sensing. High quality EEG signals of alpha, beta, delta, and gamma are measured during different activities. Moreover, the TEF-based in-ear device exhibits high reusability for over 4 months, which makes it suitable for long-term healthcare monitoring.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".