Evidence map›Paper›PMID 42303600›Full record

ArticleNature communications2026

A skin-conformal rigid-in-soft array-based imaging system.

Xiaoyun Yuan, Jianing Zhang, Youhai Liu, Yichen Wang, Lu Fang

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiaoyun Yuan *Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0002-7914-3658
Jianing Zhang *Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
Youhai LiuDepartment of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
Yichen WangDepartment of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
Lu FangDepartment of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China. fanglu@tsinghua.edu.cn.ORCID http://orcid.org/0000-0003-3552-0367

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62125106National Natural Science Foundation of China (National Science Foundation of China) 62271283National Natural Science Foundation of China (National Science Foundation of China) 62427804
6 · The paper itself

Abstract

Array-based imaging systems offer unparalleled capabilities for mapping complex environments, but their inherent structural rigidity prevents intimate integration with dynamic, non-rigid hosts such as human skin and soft robots. While flexible visual sensor arrays offer compliance, fabrication constraints severely limit their pixel density and overall resolution. Here, we present SkinSight, a skin-conformal imaging system that overcomes this trade-off through a hybrid "rigid-in-soft" architecture. By integrating miniaturized imagers with stretchable interconnects, SkinSight achieves stable, high-resolution imaging under complex mechanical deformations. Furthermore, to resolve processing challenges induced by continuous deformation, we leverage large-scale pre-trained vision transformers for end-to-end geometric inference. This deformation-adaptive framework no longer relies on rigid structural priors, enabling robust, calibration-free 3D visual perception. We demonstrate seamless integration with human skin, transforming a human palm into a sensory interface capable of perceiving objects beyond the direct line of sight. By augmenting the perceptual capabilities of humans and robots, SkinSight demonstrates broad potential for next-generation wearables and embodied artificial intelligence.

Indexed as

Imaging, Three-DimensionalRoboticsSkinEquipment DesignHumans

Identifiers

PMID42303600
PMCPMC13421666

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.