Evidence map›Paper›PMID 36772750›Full record

ArticleSensors (Basel, Switzerland)2023

Elbow Joint Stiffness Functional Scales Based on Hill's Muscle Model and Genetic Optimization.

Marija Radmilović, Djordje Urukalo, Milica M Janković, Suzana Dedijer Dujović, Tijana J Dimkić Tomić, Maja Trumić, Kosta Jovanović

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 49% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

7 authors at 2 institutions in 1 country.

Marija RadmilovićInstitute Mihailo Pupin, University of Belgrade, Volgina 15, 11060 Belgrade, Serbia.ORCID 0000-0002-5971-1427
Djordje UrukaloInstitute Mihailo Pupin, University of Belgrade, Volgina 15, 11060 Belgrade, Serbia.ORCID 0000-0002-5177-4386
Milica M JankovićSchool of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.ORCID 0000-0002-7506-4995
Suzana Dedijer DujovićClinic for Rehabilitation "Dr. Miroslav Zotović", Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
Tijana J Dimkić TomićClinic for Rehabilitation "Dr. Miroslav Zotović", Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
Maja TrumićSchool of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.ORCID 0000-0002-2945-8010
Kosta JovanovićSchool of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.ORCID 0000-0002-9029-4465
University of Belgrade · RSInstitut Mihajlo Pupin · RS

Funding

Science Fund of the Republic of Serbia, PROMIS #6062528
6 · The paper itself

Abstract

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified "Reach and retrieve" task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill's muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (

Indexed as

Elbow JointArmElbowElectromyographyIsometric ContractionMuscle, Skeletalagonist–antagonistelectromyographygenetic algorithmpassive joint structureWolf Motor Function Test

Identifiers

PMID36772750
PMCPMC9921603
OpenAlexW4319320687

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.