Evidence map›Paper›PMID 41115864›Full record

ArticleMicrosystems & nanoengineering2025

Body-coupled minimalist human-machine interface for multifunctional touch detection.

Guoliang Ma, Hu Shen, Congtian Gu, Liaoyuan Pu, Shenao Xiong, Kaixian Ba, Dakai Wang, Bin Yu, Zhiwu Han, Luquan Ren

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Guoliang MaState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China.ORCID http://orcid.org/0009-0005-0371-3799
Hu ShenState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China.
Congtian GuState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China.
Liaoyuan PuState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China.
Shenao XiongState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China.
Kaixian BaState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China. bkx@ysu.edu.cn.
Dakai WangKey Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun, 130022, China. dkwang@jlu.edu.cn.
Bin YuState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China. yb@ysu.edu.cn.
Zhiwu HanKey Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun, 130022, China.
Luquan RenKey Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun, 130022, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52305308
6 · The paper itself

Abstract

Human-machine interfaces (HMI) are of paramount importance as they serve as essential conduits between humans and the digital realm. However, contemporary designs suffer from the following issues: large number of electrodes, complex wiring, redundant data, and high power consumption. This work proposes a body-coupled minimalist human-machine interface for multifunctional touch detection (BM-HMI). The configuration of gradient resistive elements in the S-shape, in conjunction with a detection strategy founded upon the ratio of relative signal amplitudes, facilitates the effective detection of signals across a range of touch and sliding operations utilizing a mere two sensing electrodes. The experimental results demonstrate that the BM-HMI requires no battery, has remarkable stability (over 400,000 cycles), structural simplicity, rapid response time (approximately 5 ms), ultra-low detection threshold (below 0.04 N), robustness, and high scalability. This work presents a novel concept, demonstrating considerable potential for application in smart wearable devices, mixed reality systems, and ubiquitous sensing terminals.

Identifiers

PMID41115864
PMCPMC12537941

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Registered trials

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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.