Evidence map›Paper›PMID 40685978›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2025

Machine Learning-Enhanced Modular Ionic Skin for Broad-Spectrum Multimodal Discriminability in Bidirectional Human-Robot Interaction.

Qianqian Yang, Bingqiao Li, Mengke Wang, Gaoyang Pang, Yuyao Lu, Jiayan Li, Huayong Yang, Honghao Lyu, Kaichen Xu, Geng Yang

Abstract read
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

10 authors.

Qianqian YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.ORCID 0000-0002-0421-2954
Bingqiao LiPolytechnic Institute, Zhejiang University, Hangzhou, 310000, China.
Mengke WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.
Gaoyang PangSchool of Electrical and Computer Engineering, The University of Sydney, Sydney, 2006, Australia.
Yuyao LuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.
Jiayan LiState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.
Huayong YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.
Honghao LyuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.
Kaichen XuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.
Geng YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310000, China.ORCID 0000-0002-8685-5426

Funding

Bellwethers Research and Development Plan of Zhejiang Province 2023C01045Bellwethers Research and Development Plan of Zhejiang Province 2023C03007Bellwethers +X Research and Development Plan of Zhejiang Province 2024C04057(CSJ)CIE-Tencent Robotics X Rhino-Bird Focused Research ProgramDongfang Electric Corporation-Zhejiang University Joint Innovation Research InstituteNational Natural Science Foundation of China 52375031National Natural Science Foundation of China 52475610Natural Science Foundation of Zhejiang Province LDQ24E050001
6 · The paper itself

Abstract

Multimodal tactile perception systems that mimic the functionality of human skin are able to perceive complex external stimuli, facilitating advanced applications in human-machine interactions. However, current systems still struggle with limited sensing ranges and suboptimal decoupling strategies, restricting their effective multimodal sensing. To achieve broad-spectrum multimodal discriminability, a machine learning-enhanced modular ionic skin (MIS) is developed via a synergistic sensor-algorithm optimization strategy. From the sensing material perspective, process-controlled hard-segment modulation in the ionic gel enables the development of diverse ionic conductors with enhanced sensing properties: a minimum temperature coefficient of -4.00% °C

Indexed as

Machine LearningRoboticsSkinAlgorithmsHumansIonsPressureTemperatureWearable Electronic DevicesIonsbidirectional human–robot interactionbroad‐spectrum multimodal discriminabilityionic conductormachine learning

Identifiers

PMID40685978
PMCPMC12548507

What OpenQuestion holds

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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.