Evidence map›Paper›PMID 38213520›Full record

ReviewNational science review2024

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.

Kyung Rok Pyun, Kangkyu Kwon, Myung Jin Yoo, Kyun Kyu Kim, Dohyeon Gong, Woon-Hong Yeo, Seungyong Han, Seung Hwan Ko

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in National science review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
11.3field-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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 103 citations in OpenAlex.

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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

8 authors at 4 institutions in 2 countries.

Kyung Rok PyunDepartment of Mechanical Engineering, Seoul National University, Seoul08826, South Korea.
Kangkyu KwonDepartment of Mechanical Engineering, Seoul National University, Seoul08826, South Korea.
Myung Jin YooDepartment of Mechanical Engineering, Seoul National University, Seoul08826, South Korea.
Kyun Kyu KimDepartment of Chemical Engineering, Stanford University, Stanford, CA94305, USA.
Dohyeon GongDepartment of Mechanical Engineering, Ajou University, Suwon-si16499, South Korea.
Woon-Hong YeoIEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA30332, USA.
Seungyong HanDepartment of Mechanical Engineering, Ajou University, Suwon-si16499, South Korea.
Seung Hwan KoDepartment of Mechanical Engineering, Seoul National University, Seoul08826, South Korea.ORCID https://orcid.org/0000-0002-7477-0820
Seoul National University · KRAjou University · KRGeorgia Institute of Technology · USStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

Indexed as

artificial intelligencegesture recognitionhuman–machine interfacesmachine learningsoft electronicswearable sensor

Identifiers

PMID38213520
PMCPMC10776364
OpenAlexW4389097388

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.