ArticleSensors (Basel, Switzerland)2020
Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- 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
- Movement-Based Low Back Pain Subgroups Using Motion Tape Strain Data with Biomechanical and Causal Feature Engineering.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning-Based Pattern Recognition of Risk Factors for Low Back Pain among Adolescent Cricket Players in Dhaka City.PLOS digital health · 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
- Machine learning-based prediction of disability in subacute low back pain: a primary care study on clinical and psychosocial determinants.BMC medical informatics and decision making · 2026Observational
- Bioengineering Innovations for Personalized Care in Low Back Pain: From Sensors to Smart Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Lumbopelvic Kinematics During Functional Tasks in a Chronic Low Back Pain Observational Cohort.JOR spine · 2025Article
- Wearable sensor technologies for individuals with back pain: a scoping review.Pain management · 2025Article
- InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain Patients.Sensors (Basel, Switzerland) · 2025Article
- Integrating multidimensional data analytics for precision diagnosis of chronic low back pain.Scientific reports · 2025Article
- Observational
- Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.Journal of personalized medicine · 2024Review
- Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain.Scientific reports · 2024Article
- Exploring the Real-Time Variability and Complexity of Sitting Patterns in Office Workers with Non-Specific Chronic Spinal Pain and Pain-Free Individuals.Sensors (Basel, Switzerland) · 2024Article
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- Applications of Artificial Intelligence in Pain Medicine.Current pain and headache reports · 2024Review
- Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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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.