Evidence map›Paper›PMID 40573611›Full record

ArticleSensors (Basel, Switzerland)2025

Skin-Inspired Magnetoresistive Tactile Sensor for Force Characterization in Distributed Areas.

Francisco Mêda, Fabian Näf, Tiago P Fernandes, Alexandre Bernardino, Lorenzo Jamone, Gonçalo Tavares, Susana Cardoso

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Francisco MêdaINESC Microsistemas e Nanotecnologias (INESC-MN), 1000-029 Lisbon, Portugal.ORCID 0009-0001-7404-4589
Fabian NäfINESC Microsistemas e Nanotecnologias (INESC-MN), 1000-029 Lisbon, Portugal.ORCID 0000-0003-4088-4202
Tiago P FernandesINESC Microsistemas e Nanotecnologias (INESC-MN), 1000-029 Lisbon, Portugal.ORCID 0009-0006-9275-9226
Alexandre BernardinoInstituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisbon, Portugal.ORCID 0000-0003-3991-1269
Lorenzo JamoneComputer Science Department, University College London (UCL), London WC1E 6BT, UK.ORCID 0000-0002-1521-6168
Gonçalo TavaresInstituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisbon, Portugal.ORCID 0000-0002-7303-5568
Susana CardosoINESC Microsistemas e Nanotecnologias (INESC-MN), 1000-029 Lisbon, Portugal.ORCID 0000-0001-6913-6529

Funding

Fundação para a Ciência e Tecnologia BD/2020/06601
6 · The paper itself

Abstract

Touch is a crucial sense for advanced organisms, particularly humans, as it provides essential information about the shape, size, and texture of contacting objects. In robotics and automation, the integration of tactile sensors has become increasingly relevant, enabling devices to properly interact with their environment. This study aimed to develop a biomimetic, skin-inspired tactile sensor device capable of sensing applied force, characterizing it in three dimensions, and determining the point of application. The device was designed as a 4 × 4 matrix of tunneling magnetoresistive sensors, which provide a higher sensitivity in comparison to the ones based on the Hall effect, the current standard in tactile sensors. These detect magnetic field changes along a single axis, wire-bonded to a PCB and encapsulated in epoxy. This sensing array detects the magnetic field from an overlayed magnetorheological elastomer composed of Ecoflex and 5 µm neodymium-iron-boron ferromagnetic particles. Structural integrity tests showed that the device could withstand forces above 100 N, with an epoxy coverage of 0.12 mL per sensor chip. A 3D movement stage equipped with an indenting tip and force sensor was used to collect device data, which was then used to train neural network models to predict the contact location and 3D magnitude of the applied force. The magnitude-sensing model was trained on 31,260 data points, being able to accurately characterize force with a mean absolute error ranging between 0.07 and 0.17 N. The spatial sensitivity model was trained on 171,008 points and achieved a mean absolute error of 0.26 mm when predicting the location of applied force within a sensitive area of 25.5 mm × 25.5 mm using sensors spaced 4.5 mm apart. For points outside the testing range, the mean absolute error was 0.63 mm.

Indexed as

BiomimeticsBiosensing TechniquesSkinTouchEquipment DesignHumansMagnetic FieldsNeural Networks, ComputerRoboticsartificial skininstrumentationmagnetoresistive sensormagnetorheological elastomerneural networktactile sensor

Identifiers

PMID40573611
PMCPMC12197139

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.