We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Body landmarks and genetic algorithm-based approach for non-contact detection of head forward posture among Chinese adolescents: revitalizing machine learning in medicine.
- Authors
Yang, Guang; He, Shichun; Meng, Deyu; Wei, Meiqi; Cao, Jianwei; Guo, Hongzhi; Ren, He; Wang, Ziheng
- 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.
- Subjects
CHINA; CHINESE people; MACHINE learning; POSTURE; GENETIC algorithms; IDENTIFICATION
- Publication
BMC Medical Informatics & Decision Making, 2023, Vol 23, Issue 1, p1
- ISSN
1472-6947
- Publication type
Article
- DOI
10.1186/s12911-023-02285-2