Evidence map›Paper›PMID 33013656›Full record

ArticleFrontiers in neurology2020

Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?

Valentina Agostini, Marco Ghislieri, Samanta Rosati, Gabriella Balestra, Marco Knaflitz

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

36 citing papers in PubMed, 86 citations in OpenAlex.

  1. Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026
    Review
  2. Article
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  5. Review
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  10. Aging With Artificial Intelligence: How Technology Enhances Older Adults' Health and Independence.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Review
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  19. Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements.International journal of environmental research and public health · 2024
    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

5 authors at 1 institution in 1 country.

Valentina AgostiniPoliToBIOMedLab, Politecnico di Torino, Turin, Italy.
Marco GhislieriPoliToBIOMedLab, Politecnico di Torino, Turin, Italy.
Samanta RosatiPoliToBIOMedLab, Politecnico di Torino, Turin, Italy.
Gabriella BalestraPoliToBIOMedLab, Politecnico di Torino, Turin, Italy.
Marco KnaflitzPoliToBIOMedLab, Politecnico di Torino, Turin, Italy.
Polytechnic University of Turin · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements.

Indexed as

clinical practiceelectromyographyEMGlocomotionmachine learningoutcome measurementsphysical therapyrehabilitation

Identifiers

PMID33013656
PMCPMC7502709
OpenAlexW3084117216

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.