Application for Recognizing Sign Language Gestures Based on an Artificial Neural Network.

Kamil Kozyra, Karolina Trzyniec, Ernest Popardowski, Maria Stachurska

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

4 authors.

Kamil KozyraAilleron SA, Jana Pawła II 43b, 31-864 Krakow, Poland.

Karolina TrzyniecDepartment of Machinery Exploitation, Ergonomics and Production Processes, University of Agriculture in Krakow, Balicka 116B, 30-149 Krakow, Poland.

Ernest PopardowskiAilleron SA, Jana Pawła II 43b, 31-864 Krakow, Poland.ORCID 0000-0002-1225-146X

Maria StachurskaInstitute of Safety and Quality Engineering, Faculty of Management Engineering, Poznań University of Technology, J. Rychlewskiego 2, 60-965 Poznan, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents the development and implementation of an application that recognizes American Sign Language signs with the use of deep learning algorithms based on convolutional neural network architectures. The project implementation includes the development of a training set, the preparation of a module that converts photos to a form readable by the artificial neural network, the selection of the appropriate neural network architecture and the development of the model. The neural network undergoes a learning process, and its results are verified accordingly. An internet application that allows recognition of sign language based on a sign from any photo taken by the user is implemented, and its results are analyzed. The network effectiveness ratio reaches 99% for the training set. Nevertheless, conclusions and recommendations are formulated to improve the operation of the application.

Indexed as

GesturesMachine LearningAlgorithmsHumansNeural Networks, ComputerSign Languageconvolutional neural networksdeep learning algorithmmachine image recognition systems

Identifiers

PMID36560231

DOI10.3390/s22249864

PMCPMC9783182

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.