ArticleInternational journal of environmental research and public health2022
Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 7 papers.
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.
Evaluation of Lateral and Medial Part Activations of Latissimus Dorsi Muscle During Isometric Exercises in Individuals With Scoliosis
Design of a Wireless and Non-Invasive EMG-Based Embedded Measurement System for Monitoring Muscle Activation Asymmetries in Scoliosis
Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Trial
- Deep learning based vertebra localization and Cobb angle estimation using x-ray images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Agreement of MyotonPRO Measurements Across Standing and Prone Positions in Adolescents with Idiopathic Scoliosis.Journal of clinical medicine · 2026Article
- Evaluating the Effectiveness of sEMG Biofeedback for Posture Training and Scoliosis Management.BioMed research international · 2026Article
- Wearable Devices in Scoliosis Treatment: A Scoping Review of Innovations and Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- The Application of Surface Electromyography Technology in Evaluating Paraspinal Muscle Function.Diagnostics (Basel, Switzerland) · 2024Review
- Design and research of a wearable system for spinal deformity rehabilitation based on curvature and electromyography sensors.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A large number of studies have used electromyography (EMG) to measure the paraspinal muscle activity of adolescents with idiopathic scoliosis. However, investigations on the features of these muscles are very limited even though the information is useful for evaluating the effectiveness of various types of interventions, such as scoliosis-specific exercises. The aim of this cross-sectional study is to investigate the characteristics of participants with imbalanced muscle activity and the relationships among 13 features (physical features and EMG signal value). A total of 106 participants (69% with scoliosis; 78% female; 9-30 years old) are involved in this study. Their basic profile information is obtained, and the surface EMG signals of the upper trapezius, latissimus dorsi, and erector spinae (thoracic and erector spinae) lumbar muscles are tested in the static (sitting) and dynamic (prone extension position) conditions. Then, two machine learning approaches and an importance analysis are used. About 30% of the participants in this study find that balancing their paraspinal muscle activity during sitting is challenging. The most interesting finding is that the dynamic asymmetry of the erector spinae (lumbar) group of muscles is an important (third in importance) predictor of scoliosis aside from the angle of trunk rotation and height of the subject.
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What OpenQuestion holds
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.