An investigation of musculoskeletal discomforts among mining truck drivers with respect to human vibration and awkward body posture using random forest algorithm

Pub Date : 2022-07-05 DOI:10.1002/hfm.20965
Mohsen Aliabadi, Ebrahim Darvishi, Maryam Farhadian, Ramin Rahmani, Masoud Shafiee Motlagh, Neda Mahdavi
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引用次数: 2

Abstract

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.

基于随机森林算法的矿用卡车司机人体振动和笨拙身体姿势引起的肌肉骨骼不适研究
利用随机森林算法,本研究旨在研究矿用卡车司机在考虑人体振动和尴尬的身体姿势时的肌肉骨骼不适。研究对象为65名矿用卡车专业男性司机。康奈尔调查问卷用于确定肌肉骨骼不适。使用Svanteck 106a振动计测量驾驶员的振动暴露。采用快速暴露检查(QEC)对人体姿态进行分析。主要机械因素和个体危险因素作为肌肉骨骼不适模型的预测变量。与使用R Statistics Packages的多元线性回归相比,基于随机森林算法确定各特征对不舒适的相对重要性。全身振动等效加速度(WBV)高于暴露极限,手传振动等效加速度(HTV)低于暴露极限。驾驶员的身体姿势从中度到高危,需要尽快进行调查和改变。随机森林模型对驾驶员肌肉骨骼不适评分的预测误差为5.29,与回归模型的预测误差为15.92相比,处于可接受的水平。随机森林显示,笨拙的身体姿势、振动和年龄分别对肌肉骨骼不适具有最大的相对重要性。研究结果为风险因素对肌肉骨骼不适的相对重要性提供了经验证据,因此与全身振动相比,尴尬的身体姿势有更大的影响。与回归方法相比,随机森林提供了更好的输出和更准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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