Evidence map›Paper›PMID 33490046›Full record

ArticleFrontiers in bioengineering and biotechnology2020

How Well Do Commonly Used Co-contraction Indices Approximate Lower Limb Joint Stiffness Trends During Gait for Individuals Post-stroke?

Geng Li, Mohammad S Shourijeh, Di Ao, Carolynn Patten, Benjamin J Fregly

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2020. 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
2.7field-weighted citation impact, top 10% 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, 51 citations in OpenAlex.

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  12. AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025
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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 2 institutions in 1 country.

Geng LiRice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States.
Mohammad S ShourijehRice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States.
Di AoRice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States.
Carolynn PattenBiomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Davis, CA, United States.
Benjamin J FreglyRice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States.
Rice University · USUniversity of California, Davis · US

Funding

RR&D Research Career Scientist Award ApplicationIK6RX003543 · VA · VA NORTHERN CALIFORNIA HEALTH CARE SYS · PI PATTEN, CAROLYNN · 2020 to 2025
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RRD VA IK6 RX003543
6 · The paper itself

Abstract

Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCIs to approximate lower limb joint stiffness trends during gait. Calibrated EMG-driven lower extremity musculoskeletal models constructed for two individuals post-stroke were used to generate the quantities required for CCI calculations and model-based estimation of joint stiffness. CCIs were calculated for various combinations of antagonist muscle pairs based on two common CCI formulations: Rudolph et al. (2000) (

Indexed as

co-contraction indexelectromyography (EMG)EMG-driven modelingjoint stiffnessmuscle co-contraction

Identifiers

PMID33490046
PMCPMC7817819
OpenAlexW3118846672

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.