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Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach
Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on sub...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348921/ https://www.ncbi.nlm.nih.gov/pubmed/32604794 http://dx.doi.org/10.3390/s20123600 |
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author | Abdollahi, Masoud Ashouri, Sajad Abedi, Mohsen Azadeh-Fard, Nasibeh Parnianpour, Mohamad Khalaf, Kinda Rashedi, Ehsan |
author_facet | Abdollahi, Masoud Ashouri, Sajad Abedi, Mohsen Azadeh-Fard, Nasibeh Parnianpour, Mohamad Khalaf, Kinda Rashedi, Ehsan |
author_sort | Abdollahi, Masoud |
collection | PubMed |
description | Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation. |
format | Online Article Text |
id | pubmed-7348921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73489212020-07-22 Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach Abdollahi, Masoud Ashouri, Sajad Abedi, Mohsen Azadeh-Fard, Nasibeh Parnianpour, Mohamad Khalaf, Kinda Rashedi, Ehsan Sensors (Basel) Article Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation. MDPI 2020-06-26 /pmc/articles/PMC7348921/ /pubmed/32604794 http://dx.doi.org/10.3390/s20123600 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Abdollahi, Masoud Ashouri, Sajad Abedi, Mohsen Azadeh-Fard, Nasibeh Parnianpour, Mohamad Khalaf, Kinda Rashedi, Ehsan Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach |
title | Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach |
title_full | Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach |
title_fullStr | Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach |
title_full_unstemmed | Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach |
title_short | Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach |
title_sort | using a motion sensor to categorize nonspecific low back pain patients: a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348921/ https://www.ncbi.nlm.nih.gov/pubmed/32604794 http://dx.doi.org/10.3390/s20123600 |
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