Evidence map›Paper›PMID 33501300›Full record

ArticleFrontiers in robotics and AI2020

Automatic Fracture Characterization Using Tactile and Proximity Optical Sensing.

Francesca Palermo, Jelizaveta Konstantinova, Kaspar Althoefer, Stefan Poslad, Ildar Farkhatdinov

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in robotics and AI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Francesca PalermoSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.
Jelizaveta KonstantinovaSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.
Kaspar AlthoeferSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.
Stefan PosladSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.
Ildar FarkhatdinovSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper demonstrates how tactile and proximity sensing can be used to perform automatic mechanical fractures detection (surface cracks). For this purpose, a custom-designed integrated tactile and proximity sensor has been implemented. With the help of fiber optics, the sensor measures the deformation of its body, when interacting with the physical environment, and the distance to the environment's objects. This sensor slides across different surfaces and records data which are then analyzed to detect and classify fractures and other mechanical features. The proposed method implements machine learning techniques (handcrafted features, and state of the art classification algorithms). An average crack detection accuracy of ~94% and width classification accuracy of ~80% is achieved. Kruskal-Wallis results (

Indexed as

crack recognitionextreme environmentfiber-opticshaptic explorationoptical sensingsensing

Identifiers

PMID33501300
PMCPMC7805870

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.