Customizable and self-adaptive tattoo-like wearable strain sensor for human-machine interface
Bibliographic record
Abstract
• The sensor’s silly putty-inspired viscoelasticity allows it to conform to body shapes without adhesives, offering better adaptability than traditional strain sensors. • A developed sensor offering customizability, self-adaptability, longevity, moldability, self-healability, and recyclability. • Direct ink writing (DIW) can achieve complex, targeted sensor designs that can be transfer-printed onto the skin in a tattoo-like fashion . • The modular DIW sensor can be printed in general-purpose configurations and assembled for versatile customization. Flexible strain sensors have seen significant development for applications in human–machine interfaces (HMI), soft robotics, and wearable electronics. However, issues like conformability, reusability, customizability, and long-term stretchability remain. This paper introduces a customizable, self-adaptive, and biocompatible wearable strain sensor made from polyvinyl alcohol (PVA), silk fibroin (SF), and multi-walled carbon nanotubes (MWCNT) for wearable motion sensing. Inspired by the viscoelastic behaviour of Silly Putty TM , we incorporated hygroscopic calcium chloride (CaCl 2 ) to allow the sensor to absorb ambient moisture and adapt to any arbitrary surface geometries while maintaining its stretchability and sensitivity without needing adhesives. Using direct ink writing (DIW), desirable sensor geometries can be created for targeted on-body strain monitoring. A computational method is applied to identify the printing pressure and temperature ranges for optimal printability. Further, the developed sensors can be applied in a tattoo-like fashion that can be directly transfer-printed onto a suitable surface. Cyclic loading, high strain loading, and finite element analysis validated ideal conformability between the tattoo sensor and the body surface. Additionally, it is demonstrated that the developed sensor can be printed in a modular configuration, such as multiple single-line patterns. These patterns can then be assembled and personalized based on the user’s anatomy, the direction of movement, and specific functional requirements. This flexibility allows the sensor to be tailored to each application, considering all limitations and necessities, making it highly customizable. The PVA-SF-MWCNT sensor further offers exceptional longevity, self-healability, and reusability to accommodate unique wearable HMI application needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".