Evidence map›Paper›PMID 39799116›Full record

ArticleNature communications2025

An intelligent hybrid-fabric wristband system enabled by thermal encapsulation for ergonomic human-machine interaction.

Aobo Cheng, Xin Li, Ding Li, Zhikang Chen, Tianrui Cui, Lu-Qi Tao, Jinming Jian, HuiJun Xiao, Wancheng Shao, Zeyi Tang and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

15 authors.

Aobo Cheng *School of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Xin Li *School of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Ding LiSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Zhikang ChenSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Tianrui CuiSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Lu-Qi TaoSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.ORCID http://orcid.org/0000-0003-2653-2868
Jinming JianSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
HuiJun XiaoSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Wancheng ShaoSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Zeyi TangSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Xinyue LiSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Zirui DongSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China.
Houfang LiuBeijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P.R. China. houfangliu@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-3941-8936
Yi YangSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China. yiyang@tsinghua.edu.cn.
Tian-Ling RenSchool of Integrated Circuit, Tsinghua University, Beijing, P.R. China. rentl@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-7330-0544

Funding

China Postdoctoral Science Foundation 2023M731865National Natural Science Foundation of China (National Science Foundation of China) 51861145202National Natural Science Foundation of China (National Science Foundation of China) 62274101National Natural Science Foundation of China (National Science Foundation of China) U20A20168
6 · The paper itself

Abstract

Human-machine interaction has emerged as a revolutionary and transformative technology, bridging the gap between human and machine. Gesture recognition, capitalizing on the inherent dexterity of human hands, plays a crucial role in human-machine interaction. However, existing systems often struggle to meet user expectations in terms of comfort, wearability, and seamless daily integration. Here, we propose a handwriting recognition technology utilizing an intelligent hybrid-fabric wristband system. This system integrates spun-film sensors into textiles to form the smart fabric, enabling intelligent functionalities. A thermal encapsulation process is proposed to bond multiple spun-films without additional materials, ensuring the lightweight, breathability, and stretchability of the spun-film sensors. Furthermore, recognition algorithms facilitate precise accurate handwriting recognition of letters, with an accuracy of 96.63%. This system represents a significant step forward in the development of ergonomic and user-friendly wearable devices for enhanced human-machine interaction, particularly in the virtual world.

Indexed as

ErgonomicsMan-Machine SystemsTextilesWearable Electronic DevicesAlgorithmsGesturesHandwritingHumansWrist

Identifiers

PMID39799116
PMCPMC11724971

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.