BMC Medical Informatics and Decision Making (Sep 2023)

Body landmarks and genetic algorithm-based approach for non-contact detection of head forward posture among Chinese adolescents: revitalizing machine learning in medicine

  • Guang Yang,
  • Shichun He,
  • Deyu Meng,
  • Meiqi Wei,
  • Jianwei Cao,
  • Hongzhi Guo,
  • He Ren,
  • Ziheng Wang

DOI
https://doi.org/10.1186/s12911-023-02285-2
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 9

Abstract

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Abstract Addressing the current complexities, costs, and adherence issues in the detection of forward head posture (FHP), our study conducted an exhaustive epidemiologic investigation, incorporating a comprehensive posture screening process for each participant in China. This research introduces an avant-garde, machine learning-based non-contact method for the accurate discernment of FHP. Our approach elevates detection accuracy by leveraging body landmarks identified from human images, followed by the application of a genetic algorithm for precise feature identification and posture estimation. Observational data corroborates the superior efficacy of the Extra Tree Classifier technique in FHP detection, attaining an accuracy of 82.4%, a specificity of 85.5%, and a positive predictive value of 90.2%. Our model affords a rapid, effective solution for FHP identification, spotlighting the transformative potential of the convergence of feature point recognition and genetic algorithms in non-contact posture detection. The expansive potential and paramount importance of these applications in this niche field are therefore underscored.

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