Evidence map›Paper›PMID 41959644›Full record

ArticleNational science review2026

Bimodal iontronic skins powered by edge intelligence for real-time collaborative interaction.

Zhibin Li, Junli Shi, Xinxing Chen, Xingxing Chen, Ping Yang, Hanxiang He, Yuquan Leng, Xintao Huan, Han Hu, Chenglong Fu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in National science review, 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

12 authors.

Zhibin LiDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.ORCID https://orcid.org/0000-0002-1818-5766
Junli ShiDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Xinxing ChenThe Key Laboratory of Image Processing and Intelligent Control and the Hubei Key Laboratory of Brain-inspired Intelligent Systems, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Xingxing ChenDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Ping YangDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Hanxiang HeSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Yuquan LengDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Xintao HuanSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.ORCID https://orcid.org/0000-0002-6114-4994
Han HuSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Chenglong FuDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Taihong WangDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.ORCID https://orcid.org/0000-0002-6295-848X
Chuan Fei GuoDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.ORCID https://orcid.org/0000-0003-4513-3117

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Real-time sensing and processing of large-scale tactile information are crucial for enhancing the compliant interaction of embodied robots, especially in collaborative systems. However, existing robotic skin systems are limited by latency in high-throughput signal readout and intelligent reasoning, making robust real-time interaction challenging. Here, we present a flexible bimodal skin powered by edge intelligence, enabling real-time sensory perception, decision-making and actuation based on large-area coverage. The modular bimodal skin integrates pressure and temperature sensors, providing full coverage on robotic arm with over 768 pressure and 75 temperature sensor units. A rapid, crosstalk-free readout interface is implemented using a frequency-encoding architecture. Furthermore, we develop a lightweight deep learning framework that enables real-time autonomous decision-making for the bimodal skin at the edge device. We demonstrate that our system facilitates smooth, adaptive interaction for individuals with mobility impairments, even under complex or emergency conditions. This technology opens a promising avenue for real-time perception and interaction in human-centered embodied robotics.

Indexed as

deep learningelectronic skinembodied intelligencehuman–robot interactioniontronic sensor

Identifiers

PMID41959644
PMCPMC13056716

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.