Evidence map›Paper›PMID 38286796›Full record

ArticleNature communications2024

Adaptive tactile interaction transfer via digitally embroidered smart gloves.

Yiyue Luo, Chao Liu, Young Joong Lee, Joseph DelPreto, Kui Wu, Michael Foshey, Daniela Rus, Tomás Palacios, Yunzhu Li, Antonio Torralba and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. A Low Voltage-Powered Flexible Electroosmotic Actuator for Wearable Haptics.Small (Weinheim an der Bergstrasse, Germany) · 2026
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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

11 authors.

Yiyue LuoDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA. yiyueluo@mit.edu.ORCID 0009-0008-5127-0496
Chao LiuDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.
Young Joong LeeDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.
Joseph DelPretoDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.
Kui WuLightSpeed Studios, 12777 W Jefferson Boulevard, Los Angeles, CA, 90066, USA.ORCID 0000-0003-3326-7943
Michael FosheyDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.ORCID 0000-0002-9047-8387
Daniela RusDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.ORCID 0000-0001-5473-3566
Tomás PalaciosDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.
Yunzhu LiDepartment of Computer Science, University of Illinois Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL, 61801, USA.
Antonio TorralbaDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.
Wojciech MatusikDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA. wojciech@mit.edu.ORCID 0000-0003-0212-5643

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human-machine interfaces for capturing, conveying, and sharing tactile information across time and space hold immense potential for healthcare, augmented and virtual reality, human-robot collaboration, and skill development. To realize this potential, such interfaces should be wearable, unobtrusive, and scalable regarding both resolution and body coverage. Taking a step towards this vision, we present a textile-based wearable human-machine interface with integrated tactile sensors and vibrotactile haptic actuators that are digitally designed and rapidly fabricated. We leverage a digital embroidery machine to seamlessly embed piezoresistive force sensors and arrays of vibrotactile actuators into textiles in a customizable, scalable, and modular manner. We use this process to create gloves that can record, reproduce, and transfer tactile interactions. User studies investigate how people perceive the sensations reproduced by our gloves with integrated vibrotactile haptic actuators. To improve the effectiveness of tactile interaction transfer, we develop a machine-learning pipeline that adaptively models how each individual user reacts to haptic sensations and then optimizes haptic feedback parameters. Our interface showcases adaptive tactile interaction transfer through the implementation of three end-to-end systems: alleviating tactile occlusion, guiding people to perform physical skills, and enabling responsive robot teleoperation.

Indexed as

Touch PerceptionUser-Computer InterfaceFeedbackHumansTextilesTouch

Identifiers

PMID38286796
PMCPMC10825181

What OpenQuestion holds

Textmetadata
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.