Exploring EEG Based Stress in Remote Ship Operations as Foundation of Customized Training

Abstract

Remote operations have grown dramatically in popularity in recent years across countless industries thanks to technological advancements that enable human operators to monitor and control machines from distant locations. Remote operations are widely considered to be a tool that improves the performance and efficiency of ship operations. However, simply relocating humans from onboard a ship to a shore control center (SCC) and replacing existing ships with autonomous or unmanned ships does not prevent accidents or improve the efficiency of ship operations. Humans significantly affect performance, even with remote operations. This reality underscores the need for companies to employ new approaches to assessing human factors within remote ship operations. This thesis studies the human factors behind remote operations with a focus on those that impact performance and efficiency. This thesis proposes the novel smart training in remote operations (STiRO) methodology to predict the status of human factors, including mental workload and stress during remote operations, using machine learning (ML). The proposed methodology produces an interpretable ML model that accurately correlates the brain activity of human operators with the level of mental workload and stress that they perceive during remote ship operations. In this methodology, an electroencephalogram (EEG) device records the brain activity of human operators, which the ML model uses to predict human operators’ levels of stress and mental workload. In fact, interpretable ML models allow observers to infer the human factors that contribute to mental workload and stress levels. In this way, training programs can utilize the STiRO methodology to identify human factors that influence operator performance and provide customized training programs based on individual differences. In effect, the STiRO methodology enables remote operations’ stakeholders to replace traditional technical training with efficient, human-centered training and, in turn, to mitigate human factor issues during remote operations. In addition, SCC designers can utilize the STiRO methodology to identify human-human and human-machine interactions that transform into human factors issues while standardization organizations regulate SCC designs to mitigate human errors.