Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability.
Sensors (Basel)
; 24(9)2024 May 04.
Article
in En
| MEDLINE
| ID: mdl-38733035
ABSTRACT
Posture analysis is important in musculoskeletal disorder prevention but relies on subjective assessment. This study investigates the applicability and reliability of a machine learning (ML) pose estimation model for the human posture assessment, while also exploring the underlying structure of the data through principal component and cluster analyses. A cohort of 200 healthy individuals with a mean age of 24.4 ± 4.2 years was photographed from the frontal, dorsal, and lateral views. We used Student's t-test and Cohen's effect size (d) to identify gender-specific postural differences and used the Intraclass Correlation Coefficient (ICC) to assess the reliability of this method. Our findings demonstrate distinct sex differences in shoulder adduction angle (men 16.1° ± 1.9°, women 14.1° ± 1.5°, d = 1.14) and hip adduction angle (men 9.9° ± 2.2°, women 6.7° ± 1.5°, d = 1.67), with no significant differences in horizontal inclinations. ICC analysis, with the highest value of 0.95, confirms the reliability of the approach. Principal component and clustering analyses revealed potential new patterns in postural analysis such as significant differences in shoulder-hip distance, highlighting the potential of unsupervised ML for objective posture analysis, offering a promising non-invasive method for rapid, reliable screening in physical therapy, ergonomics, and sports.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Posture
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Machine Learning
Limits:
Adult
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Female
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Humans
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Male
Language:
En
Journal:
Sensors (Basel)
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: