Evidence map›Paper›PMID 38402398›Full record

SynthesisBMC psychology2024

Development and application of emotion recognition technology - a systematic literature review.

Runfang Guo, Hongfei Guo, Liwen Wang, Mengmeng Chen, Dong Yang, Bin Li

Abstract readSystematic Review
In one paragraph

Synthesis in BMC psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

6 authors.

Runfang GuoThe First Affiliated Hospital of Bengbu Medical University, Bengbu Medical University, 287 Changhuai Road, Bengbu, China.
Hongfei GuoSchool of Humanities, Southeast University, Nanjing, China.
Liwen WangSchool of Public Health, Bengbu Medical University, Bengbu, China.
Mengmeng ChenSchool of Health Management, Bengbu Medical University, Bengbu, China.
Dong YangSchool of Public Health, Bengbu Medical University, Bengbu, China.
Bin LiThe First Affiliated Hospital of Bengbu Medical University, Bengbu Medical University, 287 Changhuai Road, Bengbu, China. libin@bbmc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a mutual influence between emotions and diseases. Thus, the subject of emotions has gained increasing attention.

objectiveThe primary objective of this study was to conduct a comprehensive review of the developments in emotion recognition technology over the past decade. This review aimed to gain insights into the trends and real-world effects of emotion recognition technology by examining its practical applications in different settings, including hospitals and home environments.

methodsThis study followed the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines and included a search of 4 electronic databases, namely, PubMed, Web of Science, Google Scholar and IEEE Xplore, to identify eligible studies published between 2013 and 2023. The quality of the studies was assessed using the Critical Appraisal Skills Programme (CASP) criteria. The key information from the studies, including the study populations, application scenarios, and technological methods employed, was summarized and analyzed.

resultsIn a systematic literature review of the 44 studies that we analyzed the development and impact of emotion recognition technology in the field of medicine from three distinct perspectives: "application scenarios," "techniques of multiple modalities," and "clinical applications." The following three impacts were identified: (i) The advancement of emotion recognition technology has facilitated remote emotion recognition and treatment in hospital and home environments by healthcare professionals. (ii) There has been a shift from traditional subjective emotion assessment methods to multimodal emotion recognition methods that are grounded in objective physiological signals. This technological progress is expected to enhance the accuracy of medical diagnosis. (iii) The evolving relationship between emotions and disease throughout diagnosis, intervention, and treatment processes holds clinical significance for real-time emotion monitoring.

conclusionThese findings indicate that the integration of emotion recognition technology with intelligent devices has led to the development of application systems and models, which provide technological support for the recognition of and interventions for emotions. However, the continuous recognition of emotional changes in dynamic or complex environments will be a focal point of future research.

Indexed as

EmotionsHumansTelemedicineEmotionMedical treatmentMonitoringPatientsRecognition

Identifiers

PMID38402398
PMCPMC10894494

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.