Evidence map›Paper›PMID 32427881›Full record

ArticleScientific reports2020

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running.

Mohammad Fazle Rabbi, Claudio Pizzolato, David G Lloyd, Chris P Carty, Daniel Devaprakash, Laura E Diamond

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 pooled it
–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

64 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 more citing papers are in PubMed but not listed here.

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

6 authors.

Mohammad Fazle RabbiSchool of Allied Health Sciences, Griffith University, Gold Coast, QLD 4222, Australia.
Claudio PizzolatoSchool of Allied Health Sciences, Griffith University, Gold Coast, QLD 4222, Australia. c.pizzolato@griffith.edu.au.
David G LloydSchool of Allied Health Sciences, Griffith University, Gold Coast, QLD 4222, Australia.
Chris P CartySchool of Allied Health Sciences, Griffith University, Gold Coast, QLD 4222, Australia.
Daniel DevaprakashSchool of Allied Health Sciences, Griffith University, Gold Coast, QLD 4222, Australia.
Laura E DiamondSchool of Allied Health Sciences, Griffith University, Gold Coast, QLD 4222, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Muscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate method for muscle synergy extraction is currently unknown. In this study, we compared four muscle synergy extraction methods: non-negative matrix factorisation, principal component analysis, independent component analysis, and factor analysis. Probability distribution of muscle activation patterns were compared with the probability distribution of synergy excitation primitives obtained from the four factorisation methods. Muscle synergies extracted using non-negative matrix factorisation best matched the probability distribution of muscle activation patterns across different walking and running speeds. Non-negative matrix factorisation also best tracked changes in muscle activation patterns compared to the other factorisation methods. Our results suggest that non-negative matrix factorisation is the best factorisation method for identifying muscle synergies in dynamic tasks with different levels of muscle contraction.

Indexed as

RunningWalkingAdultFactor Analysis, StatisticalHumansMaleMuscle ContractionMuscle, SkeletalYoung Adult

Identifiers

PMID32427881
PMCPMC7237673

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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.