Evidence map›Paper›PMID 37740421›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023

Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding.

Yuyao Lu, Depeng Kong, Geng Yang, Ruohan Wang, Gaoyang Pang, Huayu Luo, Huayong Yang, Kaichen Xu

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Recent Advances in Decoupling Strategies for Soft Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Data-driven inverse design of flexible pressure sensors.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  19. Article
  20. 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

8 authors.

Yuyao LuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China.
Depeng KongState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China.
Geng YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China.ORCID 0000-0002-8685-5426
Ruohan WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China.
Gaoyang PangSchool of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
Huayu LuoState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China.
Huayong YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China.
Kaichen XuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China.ORCID 0000-0003-4957-3144

Funding

Leading innovation and entrepreneurship team project in Zhejiang 2022R01001Major Research Plan of National Natural Science Foundation of China 51890884National Natural Science Foundation of China 51975513National Natural Science Foundation of China 52105593"Pioneer" and "Leading Goose" R&D Program of Zhejiang 2023C01051"Pioneer" and "Leading Goose" R&D Program of Zhejiang 2023C03007STI 2030-Major Projects 2022ZD0208601
6 · The paper itself

Abstract

Skin-like flexible sensors play vital roles in healthcare and human-machine interactions. However, general goals focus on pursuing intrinsic static and dynamic performance of skin-like sensors themselves accompanied with diverse trial-and-error attempts. Such a forward strategy almost isolates the design of sensors from resulting applications. Here, a machine learning (ML)-guided design of flexible tactile sensor system is reported, enabling a high classification accuracy (≈99.58%) of tactile perception in six dynamic touch modalities. Different from the intuition-driven sensor design, such ML-guided performance optimization is realized by introducing a support vector machine-based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. This inverse design merges the statistical learning criteria into the design phase of sensing hardware, bridging the gap between the device structures and algorithms. Using the optimized tactile sensor, the high-quality recognizable signals in handwriting applications are obtained. Besides, with the additional data processing, a robot hand assembled with the sensor is able to complete real-time touch-decoding of an 11-digit braille phone number with high accuracy.

Indexed as

TouchTouch PerceptionHumansMachine LearningSkinhuman-machine interactionslaser-induced graphenemachine learningtactile sensortouch decoding

Identifiers

PMID37740421
PMCPMC10646241

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.