Evidence map›Paper›PMID 40162342›Full record

ArticleIEEE robotics and automation letters2025

Towards shape-adaptive attachment design for wearable devices using granular jamming.

Joseph Brignone, Logan Lancaster, Edoardo Battaglia, Haohan Zhang

Abstract read
In one paragraph

Article in IEEE robotics and automation letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

4 authors.

Joseph BrignoneDepartment of Mechanical Engineering and the Robotics Center, University of Utah, Salt Lake City, UT, USA 84112.
Logan LancasterDepartment of Mechanical Engineering and the Robotics Center, University of Utah, Salt Lake City, UT, USA 84112.
Edoardo BattagliaDepartment of Mechanical Engineering and the Robotics Center, University of Utah, Salt Lake City, UT, USA 84112.
Haohan ZhangDepartment of Mechanical Engineering and the Robotics Center, University of Utah, Salt Lake City, UT, USA 84112.

Funding

A structurally suitable neck exoskeleton for amyotrophic lateral sclerosisR03HD111884 · NICHD · UNIVERSITY OF UTAH · PI ZHANG, HAOHAN · 2024 to 2024
$148k
NICHD NIH HHS R03 HD111884
6 · The paper itself

Abstract

Attaching a wearable device to the user's body for comfort and function while accommodating the differences and changes in body shapes often represents a challenge. In this paper, we propose an approach that addresses this problem through granular jamming, where a granule-filled membrane stiffens by rapidly decreasing the internal air pressure (e.g., vacuum), causing the granule material to be jammed together due to friction. This structure was used to conform to complex shapes of the human body when it is in the soft state while switching to the rigid state for proper robot functions by jamming the granules via vacuum. We performed an experiment to systematically investigate the effect of multiple design parameters on the ability of such jamming-based interfaces to hold against a lateral force. Specifically, we developed a bench prototype where modular granular-jamming structures are attached to objects of different sizes and shapes via a downward suspension force. Our data showed that the use of jamming is necessary to increase the overall structure stability by 1.73 to 2.16 N. Furthermore, using three modules, high suspension force, and a low membrane infill (~25%) also contribute to high resistance to lateral force. Our results lay a foundation for future implementation of wearable attachments using granular-jamming structures.

Indexed as

Granular JammingPhysical Human-Robot InteractionWearable Robotics

Identifiers

PMID40162342
PMCPMC11951418

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.