Evidence map›Paper›PMID 36739411›Full record

ArticleBiomedical engineering online2023

Automatic characterization of stroke patients' posturography based on probability density analysis.

Ying Wang, Zhen Hu, Kai Chen, Ying Yang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 2 institutions in 1 country.

Ying WangSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
Zhen HuDepartment of Neurology, Ruijin Hospital Luwan Branch Affiliated to Shanghai Jiao Tong University, Shanghai, 200000, China.
Kai ChenSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
Ying YangSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China. yingyang@hdu.edu.cn.
Hangzhou Dianzi University · CNRuijin Hospital · CN

Funding

Fundamental Research Funds for the Provincial Universities of Zhejiang GK229909299001-020Zhejiang Provincial Natural Science Foundation of China LQ23F030011
6 · The paper itself

Abstract

objectiveThe probability density analysis was applied to automatically characterize the center of pressure (COP) data for evaluation of the stroke patients' balance ability.

methodsThe real-time COP coordinates of 38 stroke patients with eyes open and closed during quiet standing were obtained, respectively, from a precision force platform. The COP data were analyzed and characterized by the commonly used parameters: total sway length (SL), sway radius (SR), envelope sway area (EA), and the probability density analysis based parameters: projection area (PA), skewness (SK) and kurtosis (KT), and their statistical correlations were analyzed. The differences of both conventional parameters and probability density parameters under the conditions of eyes open (EO) and eyes closed (EC) were compared.

resultsThe PA from probability density analysis is strongly correlated with SL and SR. Both the traditional parameters and probability density parameters in the EC state are significantly different from those in the EO state. The obtained various statokinesigrams were calculated and categorized into typical sway types through probability density function for clinical evaluation of the balance ability of stroke patients.

conclusionsThe probability density analysis of COP data can be used to characterize the posturography for evaluation of the balance ability of stroke patients.

Indexed as

Postural BalanceStrokeHumansProbabilityStanding PositionBalance abilityCenter of pressurePostural controlProbability densityStroke patients

Identifiers

PMID36739411
PMCPMC9899377
OpenAlexW4319224983

What OpenQuestion holds

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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.