Evidence map›Paper›PMID 39122791›Full record

ArticleScientific reports2024

Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation.

Nicholas Tacca, Collin Dunlap, Sean P Donegan, James O Hardin, Eric Meyers, Michael J Darrow, Samuel Colachis Iv, Andrew Gillman, David A Friedenberg

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Preliminary Investigation of Myoelectric Control of an Assistive Neck Exoskeleton by Individuals With Amyotrophic Lateral Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
  7. Article
  8. Article
  9. A Low-Profile High-Density Electromyography Neckband for Recording Neck Muscle Activity.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  10. Predicting Motor Intent from Residual Neck Muscle Activity in Individuals with Neck Weakness from ALS.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  11. Review
  12. Review
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

9 authors.

Nicholas TaccaBattelle Memorial Institute, Neurotechnology, Columbus, OH, USA. tacca@battelle.org.
Collin DunlapBattelle Memorial Institute, Neurotechnology, Columbus, OH, USA.
Sean P DoneganAir Force Research Laboratory, Materials And Manufacturing Directorate, Wright-Patterson AFB, OH, USA.
James O HardinAir Force Research Laboratory, Materials And Manufacturing Directorate, Wright-Patterson AFB, OH, USA.
Eric MeyersBattelle Memorial Institute, Neurotechnology, Columbus, OH, USA.
Michael J DarrowBattelle Memorial Institute, Neurotechnology, Columbus, OH, USA.
Samuel Colachis IvBattelle Memorial Institute, Neurotechnology, Columbus, OH, USA.
Andrew Gillman *Air Force Research Laboratory, Materials And Manufacturing Directorate, Wright-Patterson AFB, OH, USA.
David A Friedenberg *Battelle Memorial Institute, Neurotechnology, Columbus, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-density electromyography (HD-EMG) can provide a natural interface to enhance human-computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with

Indexed as

ElectromyographyGesturesHandWearable Electronic DevicesAdultAlgorithmsFemaleHumansMaleMovementYoung AdultElectromyography (EMG)Human–robot collaborationMachine learningWearables

Identifiers

PMID39122791
PMCPMC11316006

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.