Evidence map›Paper›PMID 41521368›Full record

ArticleNano-micro letters2026

TENG-Based Self-Powered Silent Speech Recognition Interface: from Assistive Communication to Immersive AR/VR Interaction.

Shuai Lin, Yanmin Guo, Xiangyao Zeng, Xiongtu Zhou, Yongai Zhang, Chengda Li, Chaoxing Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nano-micro letters, 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

7 authors.

Shuai LinSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China.
Yanmin GuoSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China.
Xiangyao ZengSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China.
Xiongtu ZhouSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China.
Yongai ZhangSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China.
Chengda LiSchool of Artificial Intelligence, Xiamen City University, Xiamen, 361008, People's Republic of China. lichengda@xmcu.edu.cn.
Chaoxing WuSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China. chaoxing_wu@fzu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lip language provides a silent, intuitive, and efficient mode of communication, offering a promising solution for individuals with speech impairments. Its articulation relies on complex movements of the jaw and the muscles surrounding it. However, the accurate and real-time acquisition and decoding of these movements into reliable silent speech signals remains a significant challenge. In this work, we propose a real-time silent speech recognition system, which integrates a triboelectric nanogenerator-based flexible pressure sensor (FPS) with a deep learning framework. The FPS employs a porous pyramid-structured silicone film as the negative triboelectric layer, enabling highly sensitive pressure detection in the low-force regime (1 V N

Indexed as

AR/VR interactionDeep learningFlexible pressure sensorSilent speech recognitionTriboelectric nanogenerator

Identifiers

PMID41521368
PMCPMC12791100

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.