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Lower Limb Biomechanical Analysis of Healthy Participants
Published on: April 15, 2020
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Machine learning-enhanced back muscle strength prediction considering lifting condition and individual
Kyung-Sun Lee1, Jaejin Hwang2, Jiyeon Ha3
1Division of Energy Resources Engineering and Industrial Engineering, Kangwon National University, Republic of Korea.
International Journal of Occupational Safety and Ergonomics : JOSE
|February 19, 2025
Summary
This study found that sex, forearm posture, and lifting height significantly impact back muscle strength. These findings can inform ergonomic designs and rehabilitation to reduce work-related injuries.
Area of Science:
- Biomechanics
- Occupational Health
- Ergonomics
Background:
- Lower back pain is a common issue in manual materials handling industries.
- Back muscle strength is a key factor in preventing such injuries.
Purpose of the Study:
- To investigate the influence of sex, forearm posture, and lifting height on back muscle strength.
- To identify predictive factors for back muscle strength using machine learning.
Main Methods:
- Analysis of data from 98 participants.
- Application of machine learning models including linear regression, random forest, and multilayer perceptron (MLP).
Main Results:
- Significant effects of sex, forearm posture, and lifting height on back strength were identified.
- Males exhibited greater back muscle strength than females.
- A pronated forearm posture increased strength by 10% compared to a supinated posture.
- The MLP model demonstrated the highest predictive accuracy (r=0.896).
Conclusions:
- Findings provide insights for designing ergonomic workstations and personalized rehabilitation programs.
- Optimizing occupational safety and healthcare strategies can reduce work-related musculoskeletal disorders.
