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A video processing and machine learning based method for evaluating safety-critical operator engagement in a motorway
Linyi Jin1, Qingyu Ren2, Val Mitchell1
1School of Design and Creative Arts, Loughborough University, Loughborough, UK.
Ergonomics
|June 12, 2023
Summary
This study introduces an AI-powered system using computer vision to automatically detect operator engagement in control rooms. The novel method accurately measures engagement, improving safety in critical systems.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence
- Computer Vision
Background:
- Operator engagement is crucial for safety in critical systems.
- Existing engagement measurement methods have limitations in real-world settings.
- Automated, objective engagement detection is needed.
Purpose of the Study:
- To develop and validate a novel AI-driven methodology for evaluating operator engagement.
- To address limitations of current engagement measurement techniques.
- To enhance safety in automatic systems through better engagement monitoring.
Main Methods:
- Utilized Artificial Intelligence (AI) with computer vision (OpenCV, OpenPose) to analyze operator body posture.
- Developed a Support Vector Machine (SVM) model for engagement state classification.
- Collected data from motorway control room operators.
Main Results:
- Achieved an average accuracy of 0.89 in engagement evaluation.
- Reported weighted average precision, recall, and F1-scores above 0.84.
- Demonstrated the effectiveness of the AI-based framework.
Conclusions:
- The proposed AI methodology provides an effective, real-time, and objective measure of operator engagement.
- Accurate engagement measurement can inform interventions to improve operator performance and system safety.
- Highlights the importance of data labeling for engagement state measurement.
