Machine learning accuracy for assessment of functional movem…
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Refs2025
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Work Details
- DOI 10.1016/j.ijmedinf.2025.106155
- PMID https://pubmed.ncbi.nlm.nih.gov/41138618
- OpenAlex ID W4415255295
- Type Review
- Language EN
- Published 16 Oct 2025
- Volume 206
- Pages 106155
- Source International Journal of Medical Informa…
- Publisher Elsevier BV
- FWCI 0.14
- Countries 2
- Institutions 3
- Added 17 Oct 2025
- Updated 22 Oct 2026
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Machine learning accuracy for assessment of functional movement in Low back pain based on clinically applicable performance Metrics: A systematic review
Review 2025 EN Hybrid
• This is the first systematic review to evaluate machine learning (ML) models for assessing functional movement in people with Low back pain (LBP) using psychometric properties. • Kinematic inputs such as IMU sensors and marker-based motion capture systems are commonly used in ML models for movement assessment. • Most studies emphasised binary classification (LBP and healthy) rather than variatio…
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UN Sustainable Development Goals
1 SDGsAuthors & Affiliations
5 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Tamer Burjawi Corresponding | RMIT University (AU) | |
| 2 | Doa El‐Ansary | The University of Melbourne (AU) Shanghai University of Medicin… (CN) +1 | |
| 3 | Joshua Brodie Farragher | Shanghai University of Medicin… (CN) RMIT University (AU) | |
| 4 | Oren Tirosh | Shanghai University of Medicin… (CN) RMIT University (AU) | |
| 5 | Adrian Pranata | Shanghai University of Medicin… (CN) RMIT University (AU) |
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