Evidence map›Paper›PMID 39050486›Full record

ArticleFrontiers in robotics and AI2024

Evaluation of a passive wearable arm ExoNET.

Partha Ryali, Valentino Wilson, Courtney Celian, Adith V Srivatsa, Yaseen Ghani, Jeremy Lentz, James Patton

Abstract 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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Partha RyaliNeuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States.
Valentino WilsonNeuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States.
Courtney CelianRobotics Lab, Center for Neuroplasticity, Shirley Ryan AbilityLab, Chicago, IL, United States.
Adith V SrivatsaNeuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States.
Yaseen GhaniNeuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States.
Jeremy LentzNeuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States.
James PattonNeuro-Machine Interaction Lab, Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable ExoNETs offer a novel, wearable solution to support and facilitate upper extremity gravity compensation in healthy, unimpaired individuals. In this study, we investigated the safety and feasibility of gravity compensating ExoNETs on 10 healthy, unimpaired individuals across a series of tasks, including activities of daily living and resistance exercises. The direct muscle activity and kinematic effects of gravity compensation were compared to a sham control and no device control. Mixed effects analysis revealed significant reductions in muscle activity at the biceps, triceps and medial deltoids with effect sizes of -3.6%, -4.5%, and -7.2% rmsMVC, respectively, during gravity support. There were no significant changes in movement kinematics as evidenced by minimal change in coverage metrics at the wrist. These findings reveal the potential for the ExoNET to serve as an alternative to existing bulky and encumbering devices in post-stroke rehabilitation settings and pave the way for future clinical trials.

Indexed as

Exoskeletonsgravity compensationmedical Robotpilot Studywearable Design

Identifiers

PMID39050486
PMCPMC11265997

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.