Evidence map›Paper›PMID 42894085›Full record

ArticleNano-micro letters2026

Triboelectric E-Skin Patch Decoding Silent Speech for Advanced Human-Machine Interaction and Augmented Virtual Communication.

Ziwei Huo, Nuo Xu, Yixuan Fu, Rui Gu, Chao Liu, Zhong Lin Wang, Qijun Sun

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.

Ziwei HuoBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
Nuo XuBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
Yixuan FuBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
Rui GuBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
Chao LiuBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
Zhong Lin WangBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China. zhong.wang@mse.gatech.edu.
Qijun SunBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China. sunqijun@binn.cas.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Silent speech decoding (lip reading) represents an innovative hands-free communication method for individuals with laryngeal and lingual impairments, addressing critical daily communication challenges. Herein, a novel silent speech decoding system is proposed, which employs a self-powered, low-cost, and conformable triboelectric-based E‑skin patch and a well-trained multi-layer convolutional neural network (CNN) model. We systematically characterize the structural principles and electrical performance of the E-skin patch. A comparative analysis of labial kinematics during vocalized and silent speech is also conducted, which demonstrates strong consistency in time-frequency domain characteristics. The utilized CNN model is trained across 15 lexical categories (200 samples per category), achieving a high accuracy of 98.75% and demonstrating excellent recognition performance. Furthermore, by integrating human-machine interaction (HMI) and virtual reality (VR) technologies, we develop three representative applications, including a real-time lip language visualization system, an orofacial-controlled "Feeding Frenzy" interactive game, and a VR communication platform with avatar dialogue. This technology enables barrier-free communication for voiceless populations, demonstrating significant potential in HMI, augmentative communication systems, virtual reality integration, and biomedical rehabilitation.

Indexed as

E‑skin patchHuman–machine interactionSilent speech decodingTriboelectric sensorVirtual communication

Identifiers

PMID42894085
PMCPMC13677660

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.