Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain.

Journal: BMC musculoskeletal disorders
PMID: 38741076

Abstract

OBJECTIVES: The traditional understanding of craniocervical alignment emphasizes specific anatomical landmarks. However, recent research has challenged the reliance on forward head posture as the primary diagnostic criterion for neck pain. An advanced relationship exists between neck pain and craniocervical alignment, which requires a deeper exploration of diverse postures and movement patterns using advanced techniques, such as clustering analysis. We aimed to explore the complex relationship between craniocervical alignment, and neck pain and to categorize alignment patterns in individuals with nonspecific neck pain using the K-means algorithm.

Authors

  • Ui-Jae Hwang
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Oh-Yun Kwon
    Laboratory of Kinetic Ergocise Based on Movement Analysis, Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Jun-Hee Kim
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.

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