Evidence map›Paper›PMID 33804253›Full record

ArticleSensors (Basel, Switzerland)2021

Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality.

Aziz Siyaev, Geun-Sik Jo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
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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

2 authors.

Aziz SiyaevArtificial Intelligence Laboratory, Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea.ORCID 0000-0001-7097-7077
Geun-Sik JoArtificial Intelligence Laboratory, Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metaverses embedded in our lives create virtual experiences inside of the physical world. Moving towards metaverses in aircraft maintenance, mixed reality (MR) creates enormous opportunities for the interaction with virtual airplanes (digital twin) that deliver a near-real experience, keeping physical distancing during pandemics. 3D twins of modern machines exported to MR can be easily manipulated, shared, and updated, which creates colossal benefits for aviation colleges who still exploit retired models for practicing. Therefore, we propose mixed reality education and training of aircraft maintenance for Boeing 737 in smart glasses, enhanced with a deep learning speech interaction module for trainee engineers to control virtual assets and workflow using speech commands, enabling them to operate with both hands. With the use of the convolutional neural network (CNN) architecture for audio features and learning and classification parts for commands and language identification, the speech module handles intermixed requests in English and Korean languages, giving corresponding feedback. Evaluation with test data showed high accuracy of prediction, having on average 95.7% and 99.6% on the F1-Score metric for command and language prediction, respectively. The proposed speech interaction module in the aircraft maintenance metaverse further improved education and training, giving intuitive and efficient control over the operation, enhancing interaction with virtual objects in mixed reality.

Indexed as

Augmented RealityAircraftMaintenanceNeural Networks, ComputerSmart GlassesSpeechaircraft maintenance educationBoeing 737deep learningIndustry 4.0metaversemixed reality (MR)smart maintenancespeech interaction

Identifiers

PMID33804253
PMCPMC8001242

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.