ArticleSensors (Basel, Switzerland)2022
Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- AI-Supported Functional Pain Phenotyping in Low Back Pain Using Sensor-Based Gait and Neuromuscular Biomarkers.Sensors (Basel, Switzerland) · 2026Article
- A Multi-Sensor, Multi-Movement Exploratory Study of Motion Tape Strain Data for Low Back Pain Classification.Sensors (Basel, Switzerland) · 2026Article
- Current Trends in Artificial Intelligence for Recognizing Work Postures to Prevent Work-Related Musculoskeletal Disorders: Systematic Review and Meta-Analysis by Occupational Activity.Bioengineering (Basel, Switzerland) · 2026Review
- Bioengineering Support in the Assessment and Rehabilitation of Low Back Pain.Bioengineering (Basel, Switzerland) · 2025Review
- Classification of musculoskeletal pain using machine learning.Scientific reports · 2025Article
- Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain.Scientific reports · 2024Article
- Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Wearable Sensors Applied in Movement Analysis.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
Abstract
Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the relevance of various machine learning (ML) algorithms and Sample Entropy (SampEn), which assesses the complexity of motion variability in identifying the condition of low back pain. Twenty chronic low-back pain (CLBP) patients and 20 healthy non-LBP participants performed 1-min repetitive bending (flexion) and return (extension) trunk movements. Analysis was performed using the time series recorded by three inertial sensors attached to the participants. It was found that SampEn was significantly lower in CLBP patients, indicating a loss of movement complexity due to LBP. Gaussian Naive Bayes ML proved to be the best of the various tested algorithms, achieving 79% accuracy in identifying CLBP patients. Angular velocity of flexion movement was the most discriminative feature in the ML analysis. This study demonstrated that: supervised ML and a complexity assessment of trunk movement variability are useful in the identification of CLBP condition, and that simple kinematic indicators are sensitive to this condition. Therefore, ML could be progressively adopted by clinicians in the assessment of CLBP patients.
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Registered trials
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.