Evidence map›Paper›PMID 34451002›Full record

ReviewSensors (Basel, Switzerland)2021

Development and Progress in Sensors and Technologies for Human Emotion Recognition.

Shantanu Pal, Subhas Mukhopadhyay, Nagender Suryadevara

Open access · goldAbstract readReview
In one paragraph

Review 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 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
13.7field-weighted citation impact, top 1% 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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 103 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Article
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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

3 authors at 3 institutions in 2 countries.

Shantanu PalSchool of Computer Science, Faculty of Science, Queensland University of Technology, Brisbane, QLD 4000, Australia.ORCID 0000-0002-8784-0154
Subhas MukhopadhyaySchool of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia.ORCID 0000-0002-8600-5907
Nagender SuryadevaraSchool of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana 500046, India.ORCID 0000-0002-7786-9500
Macquarie University · AUQueensland University of Technology · AUUniversity of Hyderabad · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

Indexed as

Artificial IntelligenceCOVID-19EmotionsHumansPandemicsSARS-CoV-2human emotionInternet of Thingsmotion analysisphysiological parameters monitoringsensorswearable sensorswireless communications

Identifiers

PMID34451002
PMCPMC8402266
OpenAlexW3194028613

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.