Evidence map›Paper›PMID 24294142›Full record

ArticleTheScientificWorldJournal2013

Lateral inhibition in accumulative computation and fuzzy sets for human fall pattern recognition in colour and infrared imagery.

Antonio Fernández-Caballero, Marina V Sokolova, Juan Serrano-Cuerda

Abstract read
In one paragraph

Article in TheScientificWorldJournal, 2013. 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
–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

0 citing papers in PubMed.

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

3 authors.

Antonio Fernández-CaballeroInstituto de Investigación en Informática de Albacete (I3A), 02071 Albacete, Spain ; Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.
Marina V Sokolova
Juan Serrano-Cuerda

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fall detection is an emergent problem in pattern recognition. In this paper, a novel approach which enables to identify a type of a fall and reconstruct its characteristics is presented. The features detected include the position previous to a fall, the direction and velocity of a fall, and the postfall inactivity. Video sequences containing a possible fall are analysed image by image using the lateral inhibition in accumulative computation method. With this aim, the region of interest of human figures is examined in each image, and geometrical and kinematic characteristics for the sequence are calculated. The approach is valid in colour and in infrared video.

Indexed as

Accidental FallsColorFuzzy LogicImage Interpretation, Computer-AssistedBiomechanical PhenomenaHumansInfrared RaysPosture

Identifiers

PMID24294142
PMCPMC3833320

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.