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Lower Limb Biomechanical Analysis of Healthy Participants
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Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability.

Federico Roggio1, Sarah Di Grande2, Salvatore Cavalieri2

  • 1Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia n°97, 95123 Catania, Italy.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
Summary

Machine learning accurately assesses human posture, revealing sex-specific differences in shoulder and hip angles. This reliable, non-invasive method aids in musculoskeletal disorder prevention and screening.

Keywords:
biomechanicscluster analysisergonomicskinesiologymachine learningmusculoskeletal disordersposture analysisprincipal component analysisreliability

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Human Movement Science

Background:

  • Musculoskeletal disorder prevention is crucial but often relies on subjective posture assessments.
  • Objective and reliable human posture analysis methods are needed.

Purpose of the Study:

  • To evaluate the applicability and reliability of a machine learning (ML) pose estimation model for human posture assessment.
  • To explore data structure using principal component and cluster analyses for novel postural insights.
  • To identify gender-specific postural differences.

Main Methods:

  • Collected frontal, dorsal, and lateral photographs of 200 healthy individuals.
  • Utilized ML pose estimation for posture analysis.
  • Applied Student's t-test, Cohen's d, and Intraclass Correlation Coefficient (ICC) for statistical analysis.
  • Performed principal component and cluster analyses on postural data.

Main Results:

  • Identified significant sex differences in shoulder adduction angle (men: 16.1° ± 1.9°, women: 14.1° ± 1.5°) and hip adduction angle (men: 9.9° ± 2.2°, women: 6.7° ± 1.5°).
  • Found no significant differences in horizontal inclinations between sexes.
  • Achieved a high ICC value of 0.95, confirming the method's reliability.
  • Unsupervised ML revealed new patterns, including significant shoulder-hip distance variations.

Conclusions:

  • ML pose estimation offers a reliable and objective method for human posture analysis.
  • The approach is promising for non-invasive, rapid screening in physical therapy, ergonomics, and sports.
  • Unsupervised ML techniques can uncover novel insights into postural variations.