.png){kind=icon}
返回
Ergonomic risk level prediction framework for multiclass imbalanced data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cie.2023.109556.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Work-related Musculoskeletal Disorders (WMSDs) are a common concern in the manufacturing industry and are mostly induced by postural load requirements of job tasks that affect the neck, trunk, and upper extremities. Ergonomics assessment, such as Rapid Upper Limb Assessment (RULA), and risk prediction can help industries prevent and evaluate the risk of WMSDs. The real-world practice consists of multiple human activities that cause difficulties to assess with a common learning approach. In particular, the issue of imbalanced class distributions can degrade classification accuracy significantly. This study aims to propose a three-stage framework for pre-dicting the risk of WMSDs that handles imbalanced data. The first stage is data preparation which consists of collecting and processing data. The second stage is the evaluation of our RULA score calculation. In the last stage, we predict the risk level in two steps. The first step is to oversample the imbalanced class with an extended approach by considering relevant activities, called RD-SMOTE. The second step is to predict the risk level using our proposed network, called HyNet-CB, which is a classifier based on hybrid neural network architecture that combines Convolutional Neural Network and Bidirectional Long Short-Term Memory (HyNet-CB). We evaluate the framework using a collected dataset of an assembly process. Our framework achieved a 92.43% F1-score, and it can classify all classes with more than 80% F1-score.
Keyword:
Imbalanced class
Musculoskeletal disorders
Risk assessment
Ergonomics
RULA
期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
机构
引用论文
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysisABCDM: 一种基于注意力的双向cnn-rnn深度情感分析模型
PREAI
分享收藏An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification基于注意的离散小波变换特征增强的cnn-bilstm混合神经网络用于胎儿酸中毒分类
PREAI
分享收藏
Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容