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Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Roggio, Federico
; 
Di Grande, Sarah
; 
Cavalieri, Salvatore
; 
Falla, Deborah
; 
Musumeci, Giuseppe
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Journal article
Date
2024-05-04
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Group
Sbarro Health Research Organization (SHRO) (Temple University)
Department
Biology
Subject
Biomechanics
 Cluster analysis
 Ergonomics
 Kinesiology
 Machine learning
 Musculoskeletal disorders
 Posture analysis
 Principal component analysis
 Reliability
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https://scholarshare.temple.edu/handle/20.500.12613/12058
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https://doi.org/10.3390/s24092929
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.
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Roggio, F.; Di Grande, S.; Cavalieri, S.; Falla, D.; Musumeci, G. Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability. Sensors 2024, 24, 2929. https://doi.org/10.3390/s24092929
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Sensors, Vol. 24, Iss. 9
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