Evidence map›Paper›PMID 32385155›Full record

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

A bioinspired stretchable membrane-based compliance sensor.

Levent Beker, Naoji Matsuhisa, Insang You, Sarah Rachel Arussy Ruth, Simiao Niu, Amir Foudeh, Jeffrey B-H Tok, Xiaodong Chen, Zhenan Bao

Abstract 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 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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  12. Progress and prospects in flexible tactile sensors.Frontiers in bioengineering and biotechnology · 2023
    Review
  13. Article
  14. Article
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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.

Levent BekerDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305.
Naoji MatsuhisaDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305.
Insang YouDepartment of Materials Science and Engineering, Pohang University of Science and Technology, 37673 Pohang, Gyeongbuk, Korea.
Sarah Rachel Arussy RuthDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305.ORCID 0000-0002-6055-7471
Simiao NiuDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305.
Amir FoudehDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305.
Jeffrey B-H TokDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305.ORCID 0000-0002-2794-0663
Xiaodong ChenSchool of Materials Science and Engineering, Nanyang Technological University, 639798, Singapore.
Zhenan BaoDepartment of Chemical Engineering, Stanford University, Stanford, CA 94305; zbao@stanford.edu.ORCID 0000-0002-0972-1715

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compliance sensation is a unique feature of the human skin that electronic devices could not mimic via compact and thin form-factor devices. Due to the complex nature of the sensing mechanism, up to now, only high-precision or bulky handheld devices have been used to measure compliance of materials. This also prevents the development of electronic skin that is fully capable of mimicking human skin. Here, we developed a thin sensor that consists of a strain sensor coupled to a pressure sensor and is capable of identifying compliance of touched materials. The sensor can be easily integrated into robotic systems due to its small form factor. Results showed that the sensor is capable of classifying compliance of materials with high sensitivity allowing materials with various compliance to be identified. We integrated the sensor to a robotic finger to demonstrate the capability of the sensor for robotics. Further, the arrayed sensor configuration allows a compliance mapping which can enable humanlike sensations to robotic systems when grasping objects composed of multiple materials of varying compliance. These highly tunable sensors enable robotic systems to handle more advanced and complicated tasks such as classifying touched materials.

Indexed as

complianceelectronic skinmultimodal sensingpressure sensorstrain sensor

Identifiers

PMID32385155
PMCPMC7260970

What OpenQuestion holds

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Registered trials

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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.