Automated Workers Ergonomic Risk Assessment in Manual Material Handling using sEMG Wearable Sensors and Machine Learning
This work addresses safety for workers in material handling by providing an automated risk assessment tool, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled the problem of automatically assessing ergonomic risks in manual material handling by using sEMG wearable sensors and machine learning to detect harmful body movements, achieving up to 99.35% accuracy with a Decision Tree model.
Manual material handling tasks have the potential to be highly unsafe from an ergonomic viewpoint. Safety inspections to monitor body postures can help mitigate ergonomic risks of material handling. However, the real effect of awkward muscle movements, strains, and excessive forces that may result in an injury may not be identified by external cues. This paper evaluates the ability of surface electromyogram (EMG)-based systems together with machine learning algorithms to automatically detect body movements that may harm muscles in material handling. The analysis utilized a lifting equation developed by the U.S. National Institute for Occupational Safety and Health (NIOSH). This equation determines a Recommended Weight Limit, which suggests the maximum acceptable weight that a healthy worker can lift and carry as well as a Lifting Index value to assess the risk extent. Four different machine learning models, namely Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest are developed to classify the risk assessments calculated based on the NIOSH lifting equation. The sensitivity of the models to various parameters is also evaluated to find the best performance using each algorithm. Results indicate that Decision Tree models have the potential to predict the risk level with close to 99.35% accuracy.