Evidence map›Paper›PMID 38474953›Full record

ArticleSensors (Basel, Switzerland)2024

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System.

Carolina Gouveia, Beatriz Soares, Daniel Albuquerque, Filipa Barros, Sandra C Soares, Pedro Pinho, José Vieira, Susana Brás

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Carolina GouveiaInstituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0002-2077-2871
Beatriz SoaresInstituto de Telecomunicações, 3810-193 Aveiro, Portugal.ORCID 0000-0002-8855-6323
Daniel AlbuquerqueInstituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0002-8897-9123
Filipa BarrosCenter for Health Technology and Services Research, Department of Education and Psychology, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0003-4195-7805
Sandra C SoaresWilliam James Center for Research, Department of Education and Psychology, University of Aveiro, 3810-193 Aveiro, Portugal.
Pedro PinhoInstituto de Telecomunicações, 3810-193 Aveiro, Portugal.ORCID 0000-0001-5588-7794
José VieiraInstituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0002-4356-4522
Susana BrásInstituto de Engenharia Electrónica e Telemática de Aveiro, Departamento de Electrónica, Telecomunicações e Informática, Intelligent Systems Associate Laboratory, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0001-8650-9219

Funding

FCT/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) through national funds and when applicable co-funded by the European Union (EU) fund UIDB/50008/2020-UIDP/50008/2020 (IT)
6 · The paper itself

Abstract

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

Indexed as

RadarVital SignsAlgorithmsEmotionsRespiratory RateSignal Processing, Computer-Assistedbio-radarclassificationcontinuous waveemotional recognitionmachine learningmicro-Doppler radarpattern recognitionvital signs

Identifiers

PMID38474953
PMCPMC10935321

What OpenQuestion holds

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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.