TY  - JOUR
PY  - 2023//
TI  - A video processing and machine learning based method for evaluating safety-critical operator engagement in a motorway control room
JO  - Ergonomics
A1  - Jin, Linyi
A1  - Ren, Qingyu
A1  - Mitchell, Val
A1  - May, Andrew
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - In safety-critical automatic systems, safety can be compromised if operators lack engagement. Effective detection of undesirable engagement states can inform the design of interventions for enhancing engagement. However, the existing engagement measurement methods suffer from several limitations which damage their effectiveness in the work environment. A novel engagement evaluation methodology, which adopts Artificial Intelligence (AI) technologies, has been proposed. It was developed using motorway control room operators as subjects. Openpose and Open Source Computer Vision Library (OpenCV) were used to estimate the body postures of operators, then a Support Vector Machine (SVM) was utilized to build the engagement evaluation model based on discrete states of operator engagement. The average accuracy of the evaluation results reached 0.89 and the weighted average precision, recall and f1-score were all above 0.84. This study emphasizes the importance of specific data labelling when measuring typical engagement states, forming the basis for potential control room improvements.<p />  <p>Language: en</p>
LA  - en
SN  - 0014-0139
UR  - http://dx.doi.org/10.1080/00140139.2023.2223784
ID  - ref1
ER  -