Evidence map›Paper›PMID 39851169›Full record

ArticleACS applied materials & interfaces2025

Full Body-Worn Textile-Integrated Nanomaterials and Soft Electronics for Real-Time Continuous Motion Recognition Using Cloud Computing.

Kangkyu Kwon, Yoon Jae Lee, Suyeong Chung, Jimin Lee, Yewon Na, Youngjin Kwon, Beomjune Shin, Allison Bateman, Jaeho Lee, Matthew Guess and 3 more

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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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

13 authors.

Kangkyu KwonSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Yoon Jae LeeSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0002-4159-5966
Suyeong ChungCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Jimin LeeCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0002-8703-0979
Yewon NaCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Youngjin KwonCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0003-0182-387X
Beomjune ShinCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Allison BatemanCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Jaeho LeeCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Matthew GuessCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Jung Woo SohnSchool of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Jinwoo LeeDepartment of Mechanical, Robotics, and Energy Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Woon-Hong YeoCenter for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0002-5526-3882

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing. This system includes an array of a nanomembrane, laser-induced graphene strain sensors, and flexible electronics integrated with textiles for wireless detection of different body motions and workouts. With multiple human subjects, we demonstrate the system's performance in real-time prediction of eight different activities, including resting, walking, running, squatting, walking upstairs, walking downstairs, push-ups, and jump roping, with an accuracy of 95.3%. The class of technologies, integrated as full body-worn textile electronics and interactive pairing with smartwatches and portable devices, can be used in real-world applications such as ambulatory health monitoring via conjunction with smartwatches and feedback-enabled customized rehabilitation workouts.

Indexed as

Cloud ComputingNanostructuresTextilesWearable Electronic DevicesElectronicsGraphiteHumansMovementWireless TechnologyGraphitecloud computingdeep learningmotion recognitiontextile-integrated sensorswearable electronics

Identifiers

PMID39851169
PMCPMC11803620

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.