Evidence map›Paper›PMID 42218190›Full record

ArticleNature communications2026

An electronic fingerprint device based on spiral patterned tactile pixel array for augmented human-machine interactions.

Jie Jin, Chenhao Mao, Weijie Liu, Yang Li, Deqing Mei, Yancheng Wang

Abstract read
In one paragraph

Article in Nature communications, 2026. 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

6 authors.

Jie Jin *State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310058, China.
Chenhao Mao *Zhejiang Key Laboratory of Advanced Equipment Manufacturing and Measurement Technology, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310058, China.
Weijie LiuZhejiang Key Laboratory of Advanced Equipment Manufacturing and Measurement Technology, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310058, China.ORCID http://orcid.org/0009-0006-1603-1663
Yang LiState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310058, China.ORCID http://orcid.org/0000-0002-3118-7467
Deqing MeiState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310058, China.
Yancheng WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310058, China. yanchwang@zju.edu.cn.ORCID http://orcid.org/0000-0001-5231-6283

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52175522National Natural Science Foundation of China (National Science Foundation of China) 52475572Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation) LZ24E050003
6 · The paper itself

Abstract

Human-machine interface (HMI) devices with tactile sensing capabilities can increase both the accuracy and diversity of interaction activities. To date, the high-accuracy extraction of slippage characteristics, including both direction and displacement, remains a key challenge in HMI devices. Herein, we propose an enhanced electronic fingerprint (E-fingerprint) device that can simultaneously detect normal force, slippage direction and displacement for effective and augmented human-robot interactions. This device consists of a force-sensitive unit and a pixel array with 12×12 tactile pixels distributed in Fermat spiral pattern inspired by sunflower seeds, which highly improves the accuracy of slippage direction and displacement to 0.85° and 0.091 mm, respectively. By applying this E-fingerprint in robotic manipulation, the detected slippage can be restricted within an ideal range. A demonstration of remote writing indicates its promising performances in complicated interactive tasks. This high-accuracy E-fingerprint not only enhances the reliability of human-robot interactions but also improves interaction accuracy.

Indexed as

DermatoglyphicsMan-Machine SystemsRoboticsTouchEquipment DesignHelianthusHumans

Identifiers

PMID42218190
PMCPMC13392439

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.