Evidence map›Paper›PMID 42869920›Full record

ArticleNature communications2026

Metamaterial-enabled battery-free wireless sensor networks for unencumbered human-machine interactions.

Yimeng Zhang, Fengman He, Tianyiyi He, Qianrui Luo, Juerui Lin, Qinghao Xu, Changqing Guo, Xinyue Chai, Yuchen Wang, Tong Wu and 5 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

15 authors.

Yimeng Zhang *Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Fengman He *Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Tianyiyi He *Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, China. tianyiyi@smbu.edu.cn.ORCID http://orcid.org/0000-0003-0642-4894
Qianrui LuoInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Juerui LinInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Qinghao XuInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.ORCID http://orcid.org/0000-0001-5746-5894
Changqing GuoInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Xinyue ChaiInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Yuchen WangInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Tong WuInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Yiru JiangArtificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, China.
Chun JinInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Xiping HuArtificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, China. huxp@bit.edu.cn.ORCID http://orcid.org/0000-0002-4952-699X
Wenbo DingInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. ding.wenbo@sz.tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-0597-4512
Xi TianInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. tianxi@sz.tsinghua.edu.cn.ORCID http://orcid.org/0000-0003-1633-1210

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wireless body sensor networks could enable unencumbered human-machine interfaces for robotics, assistive healthcare, and immersive computing. However, their widespread adoption remains constrained by bulky batteries and inefficient wireless communication. Here, we report a battery-free wireless body sensor network enabled by a dual-mode metamaterial textile for continuous and unobtrusive human-machine interactions. The textile, digitally embroidered with liquid-metal fibers, establishes ultra-wideband electromagnetic pathways to simultaneously support efficient near-field power transfer and Bluetooth communication across the entire human body. This architecture enhances data throughput by 37.8 times and doubles wireless power transfer efficiency compared with emerging near-field clothing. When worn, the metamaterial-integrated clothing allows an NFC-enabled smartphone to serve as a central hub, wirelessly delivering power to and acquiring multimodal signals from distributed battery-free HMI sensors. We demonstrate that this battery-free body sensor network remains robust during motion and enables intuitive, unencumbered interactions in robotic control, virtual reality, and human digital twins.

Indexed as

Man-Machine SystemsWearable Electronic DevicesWireless TechnologyElectric Power SuppliesHumansRoboticsSmartphoneTextiles

Identifiers

PMID42869920
PMCPMC13661596

What OpenQuestion holds

Textmetadata
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.