Evidence map›Paper›PMID 42457702›Full record

ArticleNature communications2026

Stretchable multimodal deformation sensor with self-mode recognition by a single Hall sensor.

Chong Zhang, Chengfeng Pan, Neng Xia, Haojin Yang, Lin Su, Xin Wang, Zhongyi Nie, Kedong Wu, Lingxiao Zhou, Mengdi Han and 2 more

Abstract read
PubMed Full text at PMC Publisher
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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chong Zhang *State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Chengfeng Pan *State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China. cfpan@zju.edu.cn.ORCID http://orcid.org/0000-0001-8825-8011
Neng XiaDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China.ORCID http://orcid.org/0000-0001-6189-3138
Haojin YangDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China.ORCID http://orcid.org/0000-0002-1300-6300
Lin SuDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China.
Xin WangDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China.ORCID http://orcid.org/0000-0002-2373-6139
Zhongyi NieDepartment of Biomedical Engineering, Faculty of Future Technology, Peking University, Beijing, China.
Kedong WuDepartment of Biomedical Engineering, Faculty of Future Technology, Peking University, Beijing, China.
Lingxiao ZhouState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Mengdi HanDepartment of Biomedical Engineering, Faculty of Future Technology, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-7552-7398
Carmel MajidiSoft Machines Lab, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. cmajidi@andrew.cmu.edu.ORCID http://orcid.org/0000-0002-6469-9645
Li ZhangDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR, China. lizhang@cuhk.edu.hk.ORCID http://orcid.org/0000-0003-1152-8962

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stretchable sensor technologies capable of tracking multiple deformation modalities have attracted increasing attention due to their potential ability to improve the intelligence of soft robotics, stretchable electronics, and other soft-body systems. Existing multimodal deformation sensors typically require either complicated system integration and sensor layout or complex algorithmic techniques based on computational modeling and machine learning methods. Here, we report a stretchable multimodal deformation sensor that can measure and distinguish the magnitudes and modes of mechanical deformation (stretching, bending, twisting, and pressing) without reliance on complex computation or trained classification/regression models. This sensor relies on the layout of a stretchable magnetic film with a gradually varied magnetization profile and a 3-axis Hall sensor to decode the patterns of spatial change of magnetic flux strength under different deformation modes. We demonstrate the potential of this sensor in wearables and robotics by using it for neck motion monitoring, self-sensing electrical muscular stimulation, intelligent pneumatic gripper for objects classification, control of a stingray-inspired soft robot, and deformation recognition of an artificial elephant trunk.

Identifiers

PMID42457702
PMCPMC13490597

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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.