Evidence map›Paper›PMID 33869153›Full record

ArticleFrontiers in bioengineering and biotechnology2021

Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals.

Marco Recenti, Carlo Ricciardi, Romain Aubonnet, Ilaria Picone, Deborah Jacob, Halldór Á R Svansson, Sólveig Agnarsdóttir, Gunnar H Karlsson, Valdís Baeringsdóttir, Hannes Petersen and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% 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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 100 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

11 authors at 2 institutions in 1 country.

Marco RecentiInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Carlo RicciardiInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Romain AubonnetInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Ilaria PiconeInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Deborah JacobInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Halldór Á R SvanssonInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Sólveig AgnarsdóttirInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Gunnar H KarlssonInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Valdís BaeringsdóttirInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Hannes PetersenDepartment of Anatomy, University of Iceland, Reykjavík, Iceland.
Paolo GargiuloInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.
Reykjavík University · ISUniversity of Iceland · IS

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (I

Indexed as

electroencephalogram – EEGelectromyography – EMGheart ratemachine learningmotion sicknesspostural controlsea sicknessvirtual reality

Identifiers

PMID33869153
PMCPMC8047066
OpenAlexW3147407249

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.