Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
Sensors (Basel). 2023 May 10;23(10):4644. doi: 10.3390/s23104644.
To address the uncontrollable risks associated with the overreliance on ship operators' driving in current ship safety braking methods, this study aims to reduce the impact of operator fatigue on navigation safety. Firstly, this study established a human-ship-environment monitoring system with functional and technical architecture, emphasizing the investigation of a ship braking model that integrates brain fatigue monitoring using electroencephalography (EEG) to reduce braking safety risks during navigation. Subsequently, the Stroop task experiment was employed to induce fatigue responses in drivers. By utilizing principal component analysis (PCA) to reduce dimensionality across multiple channels of the data acquisition device, this study extracted centroid frequency (CF) and power spectral entropy (PSE) features from channels 7 and 10. Additionally, a correlation analysis was conducted between these features and the Fatigue Severity Scale (FSS), a five-point scale for assessing fatigue severity in the subjects. This study established a model for scoring driver fatigue levels by selecting the three features with the highest correlation and utilizing ridge regression. The human-ship-environment monitoring system and fatigue prediction model proposed in this study, combined with the ship braking model, achieve a safer and more controllable ship braking process. By real-time monitoring and prediction of driver fatigue, appropriate measures can be taken in a timely manner to ensure navigation safety and driver health.
为解决当前船舶安全制动方法中过度依赖船员驾驶所带来的不可控风险，本研究旨在降低船员疲劳对航行安全的影响。首先，本研究建立了一个具有功能和技术架构的人-船-环境监测系统，重点研究了一种船舶制动模型，该模型将脑疲劳监测与脑电图（EEG）相结合，以降低航行过程中的制动安全风险。随后，采用斯特鲁普任务实验诱导驾驶员疲劳反应。通过利用主成分分析（PCA）对数据采集设备的多个通道进行降维处理，本研究从通道 7 和 10 中提取了质心频率（CF）和功率谱熵（PSE）特征。此外，还对这些特征与疲劳严重程度量表（FSS）之间的相关性进行了分析，FSS 是用于评估受试者疲劳严重程度的五分制量表。本研究通过选择相关性最高的三个特征并利用脊回归，建立了驾驶员疲劳水平评分模型。本研究提出的人-船-环境监测系统和疲劳预测模型与船舶制动模型相结合，实现了更安全、更可控的船舶制动过程。通过对驾驶员疲劳的实时监测和预测，可以及时采取适当的措施，确保航行安全和驾驶员健康。
