Evidence map›Paper›PMID 29587375›Full record

ArticleBiosensors2018

Coverage of Emotion Recognition for Common Wearable Biosensors.

Terence K L Hui, R Simon Sherratt

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
8.9field-weighted citation impact, top 2% 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

22 citing papers in PubMed, 76 citations in OpenAlex.

  1. Article
  2. Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025
    Review
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  18. Biosensors in Occupational Safety and Health Management: A Narrative Review.International journal of environmental research and public health · 2020
    Review
  19. Review
  20. Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor.Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences · 2020
    Article
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 at 1 institution in 1 country.

Terence K L HuiDepartment of Biomedical Engineering, School of Biological Sciences, The University of Reading, Reading RG6 6AY, UK. t.k.l.hui@pgr.reading.ac.uk.ORCID 0000-0002-7513-1887
R Simon SherrattDepartment of Biomedical Engineering, School of Biological Sciences, The University of Reading, Reading RG6 6AY, UK. sherratt@ieee.org.ORCID 0000-0001-7899-4445
University of Reading · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present research proposes a novel emotion recognition framework for the computer prediction of human emotions using common wearable biosensors. Emotional perception promotes specific patterns of biological responses in the human body, and this can be sensed and used to predict emotions using only biomedical measurements. Based on theoretical and empirical psychophysiological research, the foundation of autonomic specificity facilitates the establishment of a strong background for recognising human emotions using machine learning on physiological patterning. However, a systematic way of choosing the physiological data covering the elicited emotional responses for recognising the target emotions is not obvious. The current study demonstrates through experimental measurements the coverage of emotion recognition using common off-the-shelf wearable biosensors based on the synchronisation between audiovisual stimuli and the corresponding physiological responses. The work forms the basis of validating the hypothesis for emotional state recognition in the literature and presents coverage of the use of common wearable biosensors coupled with a novel preprocessing algorithm to demonstrate the practical prediction of the emotional states of wearers.

Indexed as

Biosensing TechniquesEmotionsFemaleHumansMaleWearable Electronic Devicesbasic emotionsEDA (electrodermal activity)EMG (electromyography)emotion predictionemotion recognitionorienting responsephysiological specificityPPG (photoplethysmography)skin temperaturewearable biosensors

Identifiers

PMID29587375
PMCPMC6023004
OpenAlexW2789802223

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.