Automated Workers Ergonomic Risk Assessment in Manual Material Handling using sEMG Wearable Sensors and Machine Learning

TL;DR

This study explores the use of surface electromyogram sensors combined with machine learning algorithms to automatically assess ergonomic risks in manual material handling, aiming to improve safety monitoring.

Contribution

It introduces a novel approach integrating sEMG data with machine learning models for real-time ergonomic risk detection in manual labor tasks.

Findings

Decision Tree achieved up to 99.35% accuracy in risk prediction.

Machine learning models effectively classify ergonomic risk levels.

sEMG-based assessment outperforms traditional external cue methods.

Abstract

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.