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Related Experiment Video

Updated: Jan 12, 2026

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
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Subgrouping non-specific low back pain based on spinal marker trajectory data: An unsupervised machine learning

Hwa-Ik Yoo1, Ui-Jae Hwang2, Jong-Gook Choi3

  • 1Department of Physical Therapy, Kyungdong University, Wonju 26495, Republic of Korea.

Gait & Posture
|November 5, 2025
PubMed
Summary

Individuals with non-specific low back pain (LBP) exhibit distinct movement patterns, with both excessive and limited spinal motion potentially linked to pain adaptation. Identifying these subgroups can personalize LBP management strategies.

Keywords:
ClusteringLow back painMovement phenotypingOccupational healthSpinal kinematics

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Area of Science:

  • Biomechanical analysis
  • Movement science
  • Clinical kinesiology

Background:

  • Non-specific low back pain (LBP) is a complex condition with diverse presentations.
  • Identifying distinct movement patterns in LBP is crucial for effective management.
  • Clinically feasible assessments are needed to characterize LBP heterogeneity.

Purpose of the Study:

  • To identify movement-based subgroups in non-specific LBP using thoraco-lumbo-pelvic marker trajectories.
  • To analyze spinal movement patterns during forward bending in LBP individuals.
  • To compare movement patterns between LBP subgroups and healthy controls.

Main Methods:

  • Cross-sectional study involving 127 LBP patients and 58 healthy controls.
  • Smartphone-based video analysis of T12, L2, and S2 marker displacement during forward bending.
  • Unsupervised K-means clustering applied to kinematic features to identify movement subgroups.

Main Results:

  • Two distinct LBP subgroups identified: large-excursion (54%) and small-excursion (46%).
  • Both LBP subgroups demonstrated significant kinematic differences compared to healthy controls.
  • The small-excursion subgroup reported slightly higher pain intensity.

Conclusions:

  • Unsupervised clustering successfully identified distinct spinal movement subgroups in non-specific LBP.
  • Both excessive and limited spinal movement may represent pain-related adaptations.
  • Movement-based subgrouping is essential for tailoring LBP management approaches.