Evidence map›Paper›PMID 41169763›Full record

ArticleWearable technologies2025

Joint coordination constraints using an upper limb exoskeleton impact novel skill acquisition.

Keya Ghonasgi, Reuth Mirsky, Adrian M Haith, Peter Stone, Ashish D Deshpande

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Wearable technologies, 2025. 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

5 authors.

Keya GhonasgiDepartment of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA.ORCID https://orcid.org/0000-0002-8212-6808
Reuth MirskyDepartment of Computer Science, Tufts University, Boston, MA, USA.
Adrian M HaithDepartment of Neurology, Johns Hopkins University, Austin, TX, USA.
Peter StoneDepartment of Computer Science, The University of Texas at Austin, Austin, TX, USA.
Ashish D DeshpandeDepartment of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic exoskeletons offer the potential to train novel motor skill acquisition and thus aid physical rehabilitation. Our prior work demonstrated that individuals converge to certain kinematic coordinations as they learn a novel task. An upper-limb exoskeleton controller that constrains individuals to this known coordination was also shown to significantly improve straight-line reaching task performance. This paper studies the impact of variations of this controller on novel skill acquisition. We quantify learning under three variations of the intervention (each group with

Indexed as

exoskeletonshuman motor controlhuman–robot interactionphysical human robot interactive controllers

Identifiers

PMID41169763
PMCPMC12569393

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.