Evidence map›Paper›PMID 31263115›Full record

Trial reportScientific reports2019

Proprioceptive Sonomyographic Control: A novel method for intuitive and proportional control of multiple degrees-of-freedom for individuals with upper extremity limb loss.

Ananya S Dhawan, Biswarup Mukherjee, Shriniwas Patwardhan, Nima Akhlaghi, Guoqing Diao, Gyorgy Levay, Rahsaan Holley, Wilsaan M Joiner, Michelle Harris-Love, Siddhartha Sikdar

Open access · goldAbstract readClinical TrialMulticenter Study
In one paragraph

Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
4.6field-weighted citation impact, top 5% of its field
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

32 citing papers in PubMed, 77 citations in OpenAlex.

  1. Article
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  7. Clinical, Safety, and Engineering Perspectives on Wearable Ultrasound Technology: A Review.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024
    Review
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  19. Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2022
    Article
  20. Article
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

10 authors at 3 institutions in 1 country.

Ananya S DhawanDepartment of Computer Science, George Mason University, Fairfax, VA, 22030, USA.
Biswarup MukherjeeDepartment of Bioengineering, George Mason University, Fairfax, VA, 22030, USA.
Shriniwas PatwardhanDepartment of Bioengineering, George Mason University, Fairfax, VA, 22030, USA.ORCID http://orcid.org/0000-0001-6194-154X
Nima AkhlaghiDepartment of Bioengineering, George Mason University, Fairfax, VA, 22030, USA.
Guoqing DiaoDepartment of Statistics, George Mason University, Fairfax, VA, 22030, USA.
Gyorgy LevayInfinite Biomedical Technologies, Baltimore, MD, 21202, USA.
Rahsaan HolleyMedStar National Rehabilitation Hospital, Washington, DC, 20010, USA.
Wilsaan M JoinerDepartment of Bioengineering, George Mason University, Fairfax, VA, 22030, USA.
Michelle Harris-LoveDepartment of Bioengineering, George Mason University, Fairfax, VA, 22030, USA.
Siddhartha SikdarDepartment of Bioengineering, George Mason University, Fairfax, VA, 22030, USA. ssikdar@gmu.edu.ORCID http://orcid.org/0000-0002-6426-2320
George Mason University · USMedStar National Rehabilitation Hospital · USInfinite Biomedical Technologies (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technological advances in multi-articulated prosthetic hands have outpaced the development of methods to intuitively control these devices. In fact, prosthetic users often cite "difficulty of use" as a key contributing factor for abandoning their prostheses. To overcome the limitations of the currently pervasive myoelectric control strategies, namely unintuitive proportional control of multiple degrees-of-freedom, we propose a novel approach: proprioceptive sonomyographic control. Unlike myoelectric control strategies which measure electrical activation of muscles and use the extracted signals to determine the velocity of an end-effector; our sonomyography-based strategy measures mechanical muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Therefore, our sonomyography-based control is congruent with a prosthetic user's innate proprioception of muscle deformation in the residual limb. In this work, we evaluated proprioceptive sonomyographic control with 5 prosthetic users and 5 able-bodied participants in a virtual target achievement and holding task for 5 different hand motions. We observed that with limited training, the performance of prosthetic users was comparable to that of able-bodied participants and thus conclude that proprioceptive sonomyographic control is a robust and intuitive prosthetic control strategy.

Indexed as

AlgorithmsAmputeesArtificial LimbsElectromyographyProprioceptionUpper ExtremityAdultAgedHumansMaleMiddle Aged

Identifiers

PMID31263115
PMCPMC6602937
OpenAlexW2887073718

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.