Evidence map›Paper›PMID 41755319›Full record

ArticleSensors (Basel, Switzerland)2026

High-Resolution Contact Localization and Three-Axis Force Estimation with a Sparse Strain-Node Tactile Interface Device.

Yanyan Wu, Hanhan Wu, Yifei Han, Yi Ding, Bosheng Cao, Chongkun Xia

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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.

Yanyan WuSchool of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518005, China.ORCID 0009-0004-9802-9473
Hanhan WuSchool of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518005, China.
Yifei HanSchool of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518005, China.
Yi DingSchool of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518005, China.
Bosheng CaoSchool of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518005, China.
Chongkun XiaSchool of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518005, China.ORCID 0000-0001-5396-7643

Funding

Shenzhen Science and Technology Program JCYJ20241202123724032Shenzhen Science and Technology Program JCYJ20250604175514018The National Key R&D Program of China 2024YFB3213600
6 · The paper itself

Abstract

High-resolution contact localization and three-axis force estimation are crucial for human-robot interaction and precision manipulation, yet the sensing area is limited by channel density and wiring cost. Sparse strain readout makes joint estimation of location and three-axis force challenging due to cross-axis coupling and nonlinear responses, while dense arrays or extensive calibration increase complexity. We present a sparse strain-node tactile interface device (SSTID) whose three-module layout is optimized via particle swarm optimization to maximize informative response overlap, enabling contact localization (x,y) and three-axis force (Fx,Fy,Fz) estimation using only nine strain channels. We further propose a strain-node contact-state decoding framework (SCDF) implemented with a lightweight multilayer perceptron and trained via a two-stage sim-to-real strategy, including FEM pretraining followed by few-shot real-data adaptation. Experiments demonstrate accurate contact-state decoding with full-workspace characterization, supporting low-cost and scalable deployment of sparse tactile interfaces.

Indexed as

contact localizationfew-shot fine-tuningsim-to-real transfersparse tactile sensingthree-axis force estimation

Identifiers

PMID41755319
PMCPMC12944207

What OpenQuestion holds

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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.