Ergonomic risk level prediction framework for multiclass imbalanced data

•Ergonomics assessments help industries prevent and evaluate the risk of WMSDs.•The proposed framework predicts the risk of WMSDs and handles imbalanced data.•A hybrid neural network is proposed to predict ergonomic risk levels.•An extended oversampling method is proposed to handle the imbalanced class problem.•The framework is evaluated using a collected dataset of an assembly process. 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 predicting 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 KD-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.