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

arXiv: https://arxiv.org/abs/2109.15036 · HTML: https://mlsanity.com/paper/2109.15036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 29 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantification](https://mlsanity.com/paper/2405.13999/) (2024)
- [Fairness in Machine Learning-based Hand Load Estimation: A Case Study on Load Carriage Tasks](https://mlsanity.com/paper/2504.05610/) (2025)
- [Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study](https://mlsanity.com/paper/2204.05440/) (2022)
- [Data-Driven Ergonomic Risk Assessment of Complex Hand-intensive Manufacturing Processes](https://mlsanity.com/paper/2403.05591/) (2024)

## Abstract (arXiv)

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.