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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Vision-based work posture assessment: monocular pose estimation and fuzzy REBA methods.

Nailiang Li¹, Yu Chen¹, Zong Fang¹

¹Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu, China.

|June 12, 2026

Summary

This study introduces a contactless, vision-based system to assess worker posture risks and prevent musculoskeletal disorders (MSDs). The system uses advanced pose estimation for accurate, real-time ergonomic monitoring in industrial settings.

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Area of Science:

Occupational Health and Safety
Ergonomics
Computer Vision

Background:

Growing attention on worker health with China's National Occupational Disease Prevention Plan (2021-2025).
Need for effective methods to reduce work-related musculoskeletal disorders (MSDs).
Limitations of existing posture assessment systems in industrial environments.

Purpose of the Study:

To develop a contactless, vision-based system for posture risk assessment.
To improve the accuracy and efficiency of ergonomic monitoring.
To mitigate risks associated with musculoskeletal disorders in the workplace.

Main Methods:

Developed a contactless, vision-based system using an MMPose-based monocular pose estimation model.
Extracted skeletal landmarks for joint-angle computation.

Keywords:

Human pose estimation fuzzy logic theory machine vision rapid entire body assessment

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Published on: August 22, 2025

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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

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Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

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Employed an enhanced fuzzy-logic REBAPRO for risk assessment and utilized a system with low inference latency (6.9 ms/frame).

Main Results:

The proposed model achieved superior accuracy compared to Kinect V2, with an average joint-angle RMSE of 5.7° (vs. 10.7°).
91% of joints showed errors below 8°, indicating high precision.
Enhanced fuzzy-logic REBAPRO reduced false risk transitions by 38% and increased expert agreement from 67% to 92%.

Conclusions:

The developed vision-based system offers accurate and efficient real-time ergonomic monitoring.
It demonstrates significant potential for proactive risk mitigation of musculoskeletal disorders in industrial settings.
Successful deployment in an automotive manufacturing plant validates its practical applicability.