Evidence map›Paper›PMID 38225246›Full record

ArticleNature communications2024

Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface.

Jin Pyo Lee, Hanhyeok Jang, Yeonwoo Jang, Hyeonseo Song, Suwoo Lee, Pooi See Lee, Jiyun Kim

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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  16. Emerging Wearable Acoustic Sensing Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  17. Review
  18. 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

7 authors.

Jin Pyo LeeSchool of Material Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea.
Hanhyeok JangSchool of Material Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea.
Yeonwoo JangSchool of Material Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea.ORCID 0000-0002-8636-4432
Hyeonseo SongSchool of Material Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea.
Suwoo LeeSchool of Material Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea.
Pooi See LeeSchool of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore. pslee@ntu.edu.sg.ORCID 0000-0003-1383-1623
Jiyun KimSchool of Material Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea. jiyunkim@unist.ac.kr.ORCID 0000-0001-9756-7254

Funding

National Research Foundation of Korea (NRF) NRF-2021R1A4A3033149National Research Foundation of Korea (NRF) NRF-RS-2023-00302525
6 · The paper itself

Abstract

Human affects such as emotions, moods, feelings are increasingly being considered as key parameter to enhance the interaction of human with diverse machines and systems. However, their intrinsically abstract and ambiguous nature make it challenging to accurately extract and exploit the emotional information. Here, we develop a multi-modal human emotion recognition system which can efficiently utilize comprehensive emotional information by combining verbal and non-verbal expression data. This system is composed of personalized skin-integrated facial interface (PSiFI) system that is self-powered, facile, stretchable, transparent, featuring a first bidirectional triboelectric strain and vibration sensor enabling us to sense and combine the verbal and non-verbal expression data for the first time. It is fully integrated with a data processing circuit for wireless data transfer allowing real-time emotion recognition to be performed. With the help of machine learning, various human emotion recognition tasks are done accurately in real time even while wearing mask and demonstrated digital concierge application in VR environment.

Indexed as

EmotionsFacial ExpressionAffectFaceHumansMachine Learning

Identifiers

PMID38225246
PMCPMC10789773

What OpenQuestion holds

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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.