Evidence map›Paper›PMID 32989151›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

Near-hysteresis-free soft tactile electronic skins for wearables and reliable machine learning.

Haicheng Yao, Weidong Yang, Wen Cheng, Yu Jun Tan, Hian Hian See, Si Li, Hashina Parveen Anwar Ali, Brian Z H Lim, Zhuangjian Liu, Benjamin C K Tee

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
8.5field-weighted citation impact, top 1% of its field
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

41 citing papers in PubMed, 1 synthesis or guideline pooled it, 179 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. A Hybrid Chitosan-BaTiOACS applied engineering materials · 2026
    Article
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  16. Article
  17. Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  18. Article
  19. Article
  20. Artificial Intelligence-Powered Electronic Skin.Nature machine intelligence · 2023
    Article
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

10 authors at 2 institutions in 1 country.

Haicheng YaoMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.ORCID 0000-0003-0264-6203
Weidong YangMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
Wen ChengMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.ORCID 0000-0002-9063-1177
Yu Jun TanMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.ORCID 0000-0002-3828-9490
Hian Hian SeeMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
Si LiMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.ORCID 0000-0003-0903-7312
Hashina Parveen Anwar AliMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.ORCID 0000-0002-6900-1584
Brian Z H LimMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore.
Zhuangjian LiuInstitute of High Performance Computing, A*STAR Research Entities, 138632 Singapore, Singapore.ORCID 0000-0002-3412-2116
Benjamin C K TeeMaterials Science and Engineering, National University of Singapore, 117575 Singapore, Singapore; benjamin.tee@nus.edu.sg.ORCID 0000-0002-9180-4105
National University of Singapore · SGAgency for Science, Technology and Research · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 10

Indexed as

Machine LearningWearable Electronic DevicesHumansMaterials SciencePressurePulse Wave Analysiselectronic skinmachine learningroboticssensorwearable

Identifiers

PMID32989151
PMCPMC7568242
OpenAlexW3088723136

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.