Evidence map›Paper›PMID 39479564›Full record

ArticleFrontiers in robotics and AI2024

Novel bio-inspired soft actuators for upper-limb exoskeletons: design, fabrication and feasibility study.

Haiyun Zhang, Gabrielle Naquila, Junghyun Bae, Zonghuan Wu, Ashwin Hingwe, Ashish Deshpande

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in robotics and AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

6 authors.

Haiyun ZhangMechanical Engineering, The University of Texas at Austin, Austin, TX, United States.
Gabrielle NaquilaMechanical Engineering, The University of Texas at Austin, Austin, TX, United States.
Junghyun BaeMechanical Engineering, The University of Texas at Austin, Austin, TX, United States.
Zonghuan WuMechanical Engineering, The University of Texas at Austin, Austin, TX, United States.
Ashwin HingweMechanical Engineering, The University of Texas at Austin, Austin, TX, United States.
Ashish DeshpandeMechanical Engineering, The University of Texas at Austin, Austin, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft robots have been increasingly utilized as sophisticated tools in physical rehabilitation, particularly for assisting patients with neuromotor impairments. However, many soft robotics for rehabilitation applications are characterized by limitations such as slow response times, restricted range of motion, and low output force. There are also limited studies on the precise position and force control of wearable soft actuators. Furthermore, not many studies articulate how bellow-structured actuator designs quantitatively contribute to the robots' capability. This study introduces a paradigm of upper limb soft actuator design. This paradigm comprises two actuators: the Lobster-Inspired Silicone Pneumatic Robot (LISPER) for the elbow and the Scallop-Shaped Pneumatic Robot (SCASPER) for the shoulder. LISPER is characterized by higher bandwidth, increased output force/torque, and high linearity. SCASPER is characterized by high output force/torque and simplified fabrication processes. Comprehensive analytical models that describe the relationship between pressure, bending angles, and output force for both actuators were presented so the geometric configuration of the actuators can be set to modify the range of motion and output forces. The preliminary test on a dummy arm is conducted to test the capability of the actuators.

Indexed as

analytical modelingbio-inspired designexoskeletonindex terms-pneumatic soft actuatorswearable devices

Identifiers

PMID39479564
PMCPMC11521781

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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.