Using Random Forest algorithms, this study aimed to investigate musculoskeletal discomforts among mining truck drivers considering human vibration and awkward body posture. The study was conducted on 65 professional male drivers of mining trucks. The Cornell questionnaire was used to determine musculoskeletal discomforts. Drivers' exposure to vibrations was measured using the Svanteck 106 A vibration meter. The body posture was analyzed using the quick exposure check (QEC). The main mechanical and individual risk factors were used as predictor variables of musculoskeletal discomforts model. The relative importance of each feature on the discomforts was determined based on Random Forest algorithm compared with multiple linear regression using R Statistics Packages. The equivalent acceleration of whole-body vibration (WBV) was higher than the exposure limit, however, the equivalent acceleration of hand-transmitted vibration (HTV) was lower than the exposure limit. The body posture of drivers was from moderate to high risk so that investigation and changes are required soon. The predictive error of Random Forest model for musculoskeletal discomfort scores was at an acceptable level with root mean square error (RMSE) = 5.29 for the blind case of drivers compared with regressions model with RMSE = 15.92. Random forest showed that the awkward body posture, vibration, and age, respectively, have the greatest relative importance on musculoskeletal discomforts. The findings provide empirical evidence on the relative importance of risk factors on musculoskeletal discomfort so that awkward body posture has a greater effect compared with whole-body vibration. Random forest provided better outputs and was more accurate compared with the regression method.

中文翻译：

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使用随机森林算法调查矿用卡车司机在人体振动和尴尬身体姿势方面的肌肉骨骼不适

本研究使用随机森林算法，旨在调查考虑到人体振动和尴尬的身体姿势的矿用卡车司机的肌肉骨骼不适。该研究针对 65 名矿用卡车的职业男性司机进行。康奈尔问卷用于确定肌肉骨骼不适。使用 Svanteck 106 A 振动计测量驾驶员暴露在振动中的情况。使用快速暴露检查（QEC）分析身体姿势。主要的机械和个体风险因素被用作肌肉骨骼不适模型的预测变量。基于随机森林算法与使用 R 统计包的多元线性回归相比，确定每个特征对不适的相对重要性。全身振动等效加速度（WBV）高于暴露限值，而手传振动（HTV）等效加速度低于暴露限值。司机的身体姿势为中度至高度风险，因此需要尽快进行调查和改变。与 RMSE = 15.92 的回归模型相比，随机森林模型对肌肉骨骼不适评分的预测误差处于可接受的水平，对于驾驶员的盲人情况，均方根误差 (RMSE) = 5.29。随机森林表明，尴尬的身体姿势、振动和年龄分别对肌肉骨骼不适具有最大的相对重要性。该研究结果提供了有关风险因素对肌肉骨骼不适的相对重要性的经验证据，因此与全身振动相比，尴尬的身体姿势具有更大的影响。与回归方法相比，随机森林提供了更好的输出并且更准确。