Evidence map›Paper›PMID 32714750›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020

Machine Learning Glove Using Self-Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications.

Feng Wen, Zhongda Sun, Tianyiyi He, Qiongfeng Shi, Minglu Zhu, Zixuan Zhang, Lianhui Li, Ting Zhang, Chengkuo Lee

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.

0numbers the graph read from it
0cells of the map it votes in
83citing 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

83 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Article
  10. AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  11. Review
  12. Article
  13. Article
  14. Triboelectric Bending Sensors for AI-Enabled Sign Language Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article

23 more citing papers are in PubMed but not listed here.

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

9 authors.

Feng WenDepartment of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore.
Zhongda SunDepartment of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore.
Tianyiyi HeDepartment of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore.
Qiongfeng ShiDepartment of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore.
Minglu ZhuDepartment of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore.
Zixuan ZhangDepartment of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore.
Lianhui Lii-Lab Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences (CAS) Suzhou 215123 China.
Ting Zhangi-Lab Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences (CAS) Suzhou 215123 China.
Chengkuo LeeDepartment of Electrical & Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 Singapore.ORCID https://orcid.org/0000-0002-8886-3649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low-cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human-machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist-designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low-cost and self-powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.

Indexed as

gesture recognitionmachine learningsuperhydrophobic textilestriboelectric nanogenerators (TENGs)virtual reality/augmented reality (VR/AR) controls

Identifiers

PMID32714750
PMCPMC7375248

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.