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

Journal: Journal of neuroengineering and rehabilitation

PMID: 39501297

Abstract

BACKGROUND: Recently, 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.

Authors

Dalia Y Domínguez-Jiménez

Applied Science and Technology Institute (ICAT), National Autonomous University of Mexico (UNAM), 04510, Mexico City, Mexico.
Adriana Martínez-Hernández

Institute of Applied Research and Technology (InIAT), Universidad Iberoamericana, 01219, Mexico City, Mexico. adriana.martinez@ibero.mx.
Gustavo Pacheco-Santiago

Applied Science and Technology Institute (ICAT), National Autonomous University of Mexico (UNAM), 04510, Mexico City, Mexico.
Julio C Casasola-Vargas

Rheumatology Service Unit, General Hospital of Mexico "Dr. Eduardo Liceaga", 06720, Mexico City, Mexico.
Rubén Burgos-Vargas

Rheumatology Service Unit, General Hospital of Mexico "Dr. Eduardo Liceaga", 06720, Mexico City, Mexico.
Miguel A Padilla-Castañeda

Laboratory of Perceptual Robotics, Pisa, Scuola Superiore Sant' Anna, Italy.

Keywords

Adult Female Humans Machine Learning Male Middle Aged Movement Posture Range of Motion, Articular Spine Spondylitis, Ankylosing Wearable Electronic Devices

External Resources

View on PubMed Access via DOI PubMed (39501297)

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

Abstract

Authors

Keywords

External Resources

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