Evidence map›Paper›PMID 37963866›Full record

ArticleNature communications2023

A robotic sensory system with high spatiotemporal resolution for texture recognition.

Ningning Bai, Yiheng Xue, Shuiqing Chen, Lin Shi, Junli Shi, Yuan Zhang, Xingyu Hou, Yu Cheng, Kaixi Huang, Weidong Wang and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

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  8. Customizing Tactile Sensors via Machine Learning-Driven Inverse Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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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.

Ningning Bai *Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Yiheng Xue *Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.ORCID http://orcid.org/0009-0007-6407-1667
Shuiqing ChenDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Lin ShiDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Junli ShiDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Yuan ZhangDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Xingyu HouDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Yu ChengDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Kaixi HuangDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Weidong WangSchool of Mechano-Electronic Engineering, Xidian University, Xi'an, 710071, China.ORCID http://orcid.org/0000-0003-1713-7890
Jin ZhangDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Yuan LiuDepartment of Physics and TcSUH, University of Houston, Houston, TX, 77204, USA.
Chuan Fei GuoDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China. guocf@sustech.edu.cn.ORCID http://orcid.org/0000-0003-4513-3117

Funding

National Natural Science Foundation of China (National Science Foundation of China) No. T2225017, 52073138
6 · The paper itself

Abstract

Humans can gently slide a finger on the surface of an object and identify it by capturing both static pressure and high-frequency vibrations. Although modern robots integrated with flexible sensors can precisely detect pressure, shear force, and strain, they still perform insufficiently or require multi-sensors to respond to both static and high-frequency physical stimuli during the interaction. Here, we report a real-time artificial sensory system for high-accuracy texture recognition based on a single iontronic slip-sensor, and propose a criterion-spatiotemporal resolution, to corelate the sensing performance with recognition capability. The sensor can respond to both static and dynamic stimuli (0-400 Hz) with a high spatial resolution of 15 μm in spacing and 6 μm in height, together with a high-frequency resolution of 0.02 Hz at 400 Hz, enabling high-precision discrimination of fine surface features. The sensory system integrated on a prosthetic fingertip can identify 20 different commercial textiles with a 100.0% accuracy at a fixed sliding rate and a 98.9% accuracy at random sliding rates. The sensory system is expected to help achieve subtle tactile sensation for robotics and prosthetics, and further be applied to haptic-based virtual reality and beyond.

Identifiers

PMID37963866
PMCPMC10645869

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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.