Evidence map›Paper›PMID 32443736›Full record

ArticleSensors (Basel, Switzerland)2020

A New Automatic Foot Arch Index Measurement Method Based on a Flexible Membrane Pressure Sensor.

Tao Zheng, Zhiyong Yu, Jin Wang, Guodong Lu

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 16 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Tao ZhengState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
Zhiyong YuState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.ORCID 0000-0003-1139-3842
Jin WangState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
Guodong LuState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The foot arch index is an important index to evaluate the health of human feet and the biomechanics line, aiming at addressing the shortcomings of the low efficiency and slow speed of manual foot arch index measurement; in this work, an automatic foot arch index measurement method based on a flexible membrane pressure sensor was proposed. The distribution of plantar pressure data was obtained from the flexible membrane pressure sensor and converted into a digital image. The 8-neighborhood correlation pixel method was proposed to remove the interference of isolated noise points. In order to remove the toes' data without affecting the foot sole data, the row element association algorithm was proposed. The front and back endpoints of the foot were automatically located to obtain the foot length, and the foot arch index was also automatically obtained based on the foot arch pressure area. Whether it was a high arch foot, flat foot or normal foot, the method proposed in this paper could accurately and quickly distinguish them. The prototype was developed, and its feasibility and validity were verified by a series of tests.

Indexed as

AlgorithmsPressureBiomechanical PhenomenaFlatfootFootHumans8-neighborhood correlation pixelflat footflexible membrane pressure sensorfoot arch indexhigh arch footimage processingplantar pressurerow element association

Identifiers

PMID32443736
PMCPMC7285023
OpenAlexW3027807786

What OpenQuestion holds

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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.