Evidence map›Paper›PMID 39501297›Full record

ArticleJournal of neuroengineering and rehabilitation2024

A machine learning approach for the design optimization of a multiple magnetic and inertial sensors wearable system for the spine mobility assessment.

Dalia Y Domínguez-Jiménez, Adriana Martínez-Hernández, Gustavo Pacheco-Santiago, Julio C Casasola-Vargas, Rubén Burgos-Vargas, Miguel A Padilla-Castañeda

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2024. 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
–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

1 citing paper in PubMed.

  1. Review
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.

Dalia Y Domínguez-JiménezApplied Science and Technology Institute (ICAT), National Autonomous University of Mexico (UNAM), 04510, Mexico City, Mexico.
Adriana Martínez-HernándezInstitute of Applied Research and Technology (InIAT), Universidad Iberoamericana, 01219, Mexico City, Mexico. adriana.martinez@ibero.mx.
Gustavo Pacheco-SantiagoApplied Science and Technology Institute (ICAT), National Autonomous University of Mexico (UNAM), 04510, Mexico City, Mexico.
Julio C Casasola-VargasRheumatology Service Unit, General Hospital of Mexico "Dr. Eduardo Liceaga", 06720, Mexico City, Mexico.
Rubén Burgos-VargasRheumatology Service Unit, General Hospital of Mexico "Dr. Eduardo Liceaga", 06720, Mexico City, Mexico.
Miguel A Padilla-CastañedaApplied Science and Technology Institute (ICAT), National Autonomous University of Mexico (UNAM), 04510, Mexico City, Mexico. miguel.padilla@icat.unam.mx.

Funding

DGAPA-PAPIIT UNAM TA100920Secretaria de Educación, Ciencia, Tecnología e Innovación 219/20119
6 · The paper itself

Abstract

backgroundRecently, magnetic and inertial measurement units (MIMU) based systems have been applied in the spine mobility assessment; this evaluation is essential in the clinical practice for diagnosis and treatment evaluation. The available systems are limited in the number of sensors, and neither develops a methodology for the correct placement of the sensors, seeking the relevant mobility information of the spine.

methodsThis work presents a methodology for analyzing a system consisting of sixteen MIMUs to reduce the amount of information and obtain an optimal configuration that allows distinguishing between different body postures in a movement. Four machine learning algorithms were trained and assessed using data from the range of motion in three movements (Mov.1-Anterior hip flexion; Mov.2-Lateral trunk flexion; Mov.3-Axial trunk rotation) obtained from 12 patients with Ankylosing Spondylitis.

resultsThe methodology identified the optimal minimal configuration for different movements. The configuration showed good accuracy in discriminating between different body postures. Specifically, it had an accuracy of 0.963 ± 0.021 for detecting when the subject is upright or bending in Mov.1, 0.944 ± 0.038 for identifying when the subject is flexed to the left or right in Mov.2, and 0.852 ± 0.097 for recognizing when the subject is rotated to the right or left in Mov.3.

conclusionsOur results indicate that the methodology developed results in a feasible configuration for practical clinical studies and paves the way for designing specific IMU-based assessment instruments.

trial registrationStudy approved by the Local Ethics Committee of the General Hospital of Mexico "Dr. Eduardo Liceaga" (protocol code DI/03/17/471).

Indexed as

Machine LearningRange of Motion, ArticularSpineWearable Electronic DevicesAdultFemaleHumansMaleMiddle AgedMovementPostureSpondylitis, AnkylosingBiomechanicsInertial measurement unitsMachine learningMusculoskeletal DisordersSpine

Identifiers

PMID39501297
PMCPMC11536966

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Registered trials

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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.