Evidence map›Paper›PMID 42369338›Full record

ArticleNational science review2026

Textile emitter for AI-enhanced human-machine interaction.

Ya Sun, Xuanyu Cui, Gaoyang Kong, Jiaxin Li, Xiaomei Chen, Jianing Xu, Chengxin Xu, Peng Jin, Tao Hou, Hui Pan 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. 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.

Ya SunState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Xuanyu CuiState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Gaoyang KongState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Jiaxin LiDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore  117583, Singapore.
Xiaomei ChenDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore  117583, Singapore.
Jianing XuState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Chengxin XuState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.
Peng JinDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore  117583, Singapore.
Tao HouDepartment of Physics, Xiamen University, Xiamen 361005, China.
Hui PanState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Di ZhangState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Han ZhouState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable textile interfaces hold significant promise for advanced human-machine interaction, yet realizing wireless, battery-free on-body signal generation without embedded circuitry in textiles remains a formidable challenge. Here, we present a textile emitter that enables precise interaction by directly transducing user-induced mechanical deformation into time-coded infrared (IR) thermal signals. Constructed from strain-responsive IR-emitting fibers, the textile emitter simultaneously functions as a programmable signal generator without embedded electronic circuits or power sources within the textile. Seamlessly integrated into everyday garments, it ensures stable performance while maintaining comfort and wearability. Inspired by Morse code, user motions are encoded as temporally structured IR signals and wirelessly decoded by machine learning algorithms, achieving up to 99.6% recognition accuracy across diverse interaction commands. This AI-enhanced textile emitter establishes a scalable and robust foundation for future wearable systems in information communication, robotic control, and digital interaction.

Indexed as

AI-enhanced human–machine interactionsmart photonic textilesstrain-responsive infrared emissiontextile emitterswireless interaction

Identifiers

PMID42369338
PMCPMC13296564

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.