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An ergonomic evaluation using a deep learning approach for assessing postural risks in a virtual reality-based smart

Suman Kalyan Sardar¹, Seul Chan Lee²

¹Department of Mathematics & Computer Science, University of Bremen, Bremen, Germany.

|May 14, 2024

Summary

This study uses deep learning and virtual reality to identify unsafe worker postures in smart manufacturing. It assesses risks using established ergonomic tools, aiding managers in understanding and mitigating hazards.

Keywords:

Ergonomics REBA RULA deep learning virtual reality

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Area of Science:

Ergonomics
Industrial Engineering
Computer Vision

Background:

Industrial work in smart manufacturing presents risks of unsafe worker postures.
Accurate identification and assessment of these postural risks are crucial for worker safety and productivity.
Current methods may not fully capture the dynamic nature of postures in advanced manufacturing environments.

Purpose of the Study:

To propose an integrated ergonomic evaluation framework for identifying unsafe postures in virtual reality-based smart manufacturing.
To assess postural risks using a combination of computer vision, deep learning, and established ergonomic assessment tools.
To analyze the directional movements contributing to the most unsafe postures.

Main Methods:

Utilized a deep learning (DL) convolutional neural network approach with computer vision to detect body keypoint displacements and estimate the centre of mass (COM).
Applied ergonomic risk assessment tools, including Rapid Upper Limb Assessment (RULA) and Rapid Whole-Body Assessment (RWBA), to quantify risk levels.
Conducted an analysis of variance (ANOVA) to compare vertical and horizontal postural movement directions.

Main Results:

Successfully identified and assessed ergonomic risk levels associated with unsafe postures in smart manufacturing settings.
The DL-based computer vision method effectively recognized postural deviations.
Significant differences were found in postural movements contributing to high-risk postures.

Conclusions:

The integrated ergonomic evaluation effectively identifies and quantifies postural risks in smart manufacturing.
Deep learning and virtual reality integration offers a robust method for analyzing worker ergonomics.
Findings provide valuable insights for industry managers and researchers to improve workplace safety.