Evidence map›Paper›PMID 40078976›Full record

ReviewRSC advances2025

Recent advancements in wearable sensors: integration with machine learning for human-machine interaction.

Guangrui Mu, Yang Zhang, Zhonghong Yan, Qinming Yu, Qifan Wang

Abstract readReview
In one paragraph

Review in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

5 authors.

Guangrui MuInstitute of Higher Education of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine Harbin PR China guangruimu26@163.com.ORCID https://orcid.org/0009-0001-5003-5258
Yang ZhangInstitute of Higher Education of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine Harbin PR China guangruimu26@163.com.
Zhonghong YanGraduate School, Heilongjiang University of Chinese Medicine Harbin PR China.
Qinming YuSchool of Humanities and Management, Heilongjiang University of Chinese Medicine Harbin PR China.
Qifan WangSchool of Humanities and Management, Heilongjiang University of Chinese Medicine Harbin PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable sensors have emerged as a transformative technology, enabling real-time monitoring and advanced functionality in various fields, including healthcare, human-machine interaction, and environmental sensing. This review provides a comprehensive overview of the latest advancements in wearable sensor technologies, focusing on innovations in sensor design, material flexibility, and integration with machine learning. We explore the feasibility of wearable electronics in achieving high-performance, flexible devices and discuss their potential to enhance human-machine interactions through intelligent data processing and decision-making. The combination of wearable electronics and machine learning offers immense potential for applications requiring real-time responsiveness and advanced analytics. By analyzing recent developments of sensors, this review aims to inspire further innovations in this rapidly evolving field, paving the way for the next generation of wearable technologies.

Identifiers

PMID40078976
PMCPMC11897882

What OpenQuestion holds

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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.