Evidence map›Paper›PMID 38534862›Full record

ArticleBiomimetics (Basel, Switzerland)2024

How to Easily Make Self-Sensing Pneumatic Inverse Artificial Muscles.

Valentina Potnik, Gabriele Frediani, Federico Carpi

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. 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. Article
  2. 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

3 authors.

Valentina PotnikBiomedical Engineering Unit, Department of Industrial Engineering, University of Florence, 50121 Florence, Italy.
Gabriele FredianiBiomedical Engineering Unit, Department of Industrial Engineering, University of Florence, 50121 Florence, Italy.ORCID 0000-0002-7514-2906
Federico CarpiBiomedical Engineering Unit, Department of Industrial Engineering, University of Florence, 50121 Florence, Italy.ORCID 0000-0001-8496-5085

Funding

European Commission HORIZON-MSCA-2021, Project 101072920-SOFTWEARItalian Ministry of University and Research PNRR projects "AGE-IT" and "Fit4MedRob"
6 · The paper itself

Abstract

Wearable mechatronics for powered orthoses, exoskeletons and prostheses require improved soft actuation systems acting as 'artificial muscles' that are capable of large strains, high stresses, fast response and self-sensing and that show electrically safe operation, low specific weight and large compliance. Among the diversity of soft actuation technologies under investigation, pneumatic devices have been the focus, during the last couple of decades, of renewed interest as an intrinsically soft artificial muscle technology, due to technological advances stimulated by applications in soft robotics. As of today, quite a few solutions are available to endow a pneumatic soft device with linear actuation and self-sensing ability, while also easily achieving these features with off-the-shelf materials and low-cost fabrication processes. Here, we describe a simple process to make self-sensing pneumatic actuators, which may be used as 'inverse artificial muscles', as, upon pressurisation, they elongate instead of contracting. They are made of an elastomeric tube surrounded by a plastic coil, which constrains radial expansions. As a novelty relative to the state of the art, the self-sensing ability was obtained with a piezoresistive stretch sensor shaped as a conductive elastomeric body along the tube's central axis. Moreover, we detail, also by means of video clips, a step-by-step manufacturing process, which uses off-the-shelf materials and simple procedures, so as to facilitate reproducibility.

Indexed as

actuatorartificial musclepneumaticself-sensing

Identifiers

PMID38534862
PMCPMC10968065

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.