Skin-printable and self-adhesive hydrogel electrodes for functional electrical stimulation therapy
Bibliographic record
Abstract
From smart skins to human-machine interfaces, soft conductive materials have immense potential in wearable applications because of their conformity to the human skin. These materials may be adapted for healthcare devices and sensing functionalities, as well as for rehabilitation purposes like surface functional electrical stimulation (sFES), the process of inducing contractions in paralyzed muscles with electric currents. However, variabilities in muscle distribution among individuals pose new challenges against the development of wearable and personalized sFES platforms. To account for the intricate differences between muscles on different sites of the body, we developed a novel material to actualize stimulation electrodes that are adaptable to be of any shape and size, with self-adhesive properties to ensure conformity to body morphology and guarantee stimulation signal stability. The bio-based polymer of carboxymethyl cellulose is used for the hydrogel matrix due to its water solubility, along with poly(3,4- ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as the conductive additive and tannic acid as the adhesive additive. A mild and biocompatible gelation method involving hydrogen bonds is implemented via the addition of phytic acid, forming the printed hydrogel for the bioelectronic interface within minutes. Compared with conventional stimulation electrodes, the printable hydrogel electrodes can induce muscle movement during sFES with better precision and accuracy.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".