ArticleSensors (Basel, Switzerland)2022
Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain.
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 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- A Multi-Sensor, Multi-Movement Exploratory Study of Motion Tape Strain Data for Low Back Pain Classification.Sensors (Basel, Switzerland) · 2026Article
- Classification of musculoskeletal pain using machine learning.Scientific reports · 2025Article
- Does Pain Self-efficacy influence Initial Forward Bending in Adults with Chronic Low Back Pain following Exercise? A Cohort Study.International journal of sports physical therapy · 2025Article
- Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain.Scientific reports · 2024Article
- Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain.BMC musculoskeletal disorders · 2024Article
- Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain.Sensors (Basel, Switzerland) · 2024Article
- Left and Right Cortical Activity Arising from Preferred Walking Speed in Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Wearable Sensors Applied in Movement Analysis.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy.
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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.