Evidence map›Paper›PMID 42106861›Full record

ArticleJournal of neuroengineering and rehabilitation2026

Hands-free control of an assistive robotic arm for high-level paralysis.

Brady A Hasse, Mario A Ibarra, Jehad A Alfaleh, Derek Gin, Drew E G Sheets, Aaliyah M P A Thompson-Mazzeo, Andrew J Fuglevand

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2026. 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

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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.

Brady A Hasse *Departments of Physiology and Neuroscience, College of Medicine, University of Arizona, Tucson, USA.
Mario A Ibarra *Departments of Physiology and Neuroscience, College of Medicine, University of Arizona, Tucson, USA.
Jehad A AlfalehDepartments of Physiology and Neuroscience, College of Medicine, University of Arizona, Tucson, USA.
Derek GinDepartments of Physiology and Neuroscience, College of Medicine, University of Arizona, Tucson, USA.
Drew E G SheetsDepartments of Physiology and Neuroscience, College of Medicine, University of Arizona, Tucson, USA.
Aaliyah M P A Thompson-MazzeoDepartments of Physiology and Neuroscience, College of Medicine, University of Arizona, Tucson, USA.
Andrew J FuglevandDepartments of Physiology and Neuroscience, College of Medicine, University of Arizona, Tucson, USA. fuglevan@email.arizona.edu.ORCID http://orcid.org/0000-0002-4349-6478

Funding

NIH HHS NS130397
6 · The paper itself

Abstract

backgroundRecent advancements in assistive robotic arms have enabled many people with tetraplegia to perform activities of daily living more independently. Because these systems typically require hand use, they are not a ready option for many individuals with high-level (C4 and above) tetraplegia. Such individuals, however, might be able to use signals that arise from the head and neck to control assistive devices. Therefore, the goal of the study was to evaluate the utility of several signals arising from the head and neck to control a robotic arm during 3D center-out reaching to multiple targets ~ 25-50 cm from the start location.

methodsTen non-disabled human subjects were tested using five non-invasive, hands-free modalities (head position, head velocity, facial electromyography, tongue, and voice) to control a robot arm. For comparison, subjects also used joystick position and joystick velocity methods to control reaching movements of the robotic arm. A one-way repeated measures ANOVA was carried out on key performance indicators including movement time, path efficiency, throughput, and perceived workload.

resultsThe hands-free control modalities of head position, facial EMG, tongue, and voice had average (± SD) movement times (5.8 ± 1.6, 8.2 ± 3.7, 6.3 ± 2.0, and 10.0 ± 3.7 s, respectively). With the exception of voice, none of these times were significantly different than that of the benchmark hand position control of a joystick (6.3 ± 2.3 s). Furthermore, no significant differences were revealed in perceived workload across control modalities.

conclusionsThese results indicate, therefore, that various non-invasive, hands-free methods could be used effectively by people with high-level tetraplegia to operate assistive robotic arms.

Indexed as

ArmQuadriplegiaRoboticsSelf-Help DevicesAdultElectromyographyFemaleHumansMaleTongueYoung AdultAssistive roboticsFacial EMGHead movementsReachingRobotic armTetraplegiaTongueVoice

Identifiers

PMID42106861
PMCPMC13339319

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LicenceCC BY-NC-ND
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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.