부산대학교 도서관

To ensure the safety of marine traffic, ship watchkeeping officers must maintain a high level of vigilance during their watchkeeping period. Although advanced driving assistance systems are available that can effectively measure driver alertness and provide early warnings before any hazardous maneuvers, such systems, are primarily designed for road vehicles and are considerably different from those used when operating a marine vehicle. In this article, we propose to use fine-grained channel state information (CSI) obtained using commercial off-the-shelf wireless fidelity (Wi-Fi) to track an officer-on-watch’s vigilant activities and determine, in real time, whether the ship officer is adhering to safety guidelines. We developed a CSI path model and a 2-D multiple signal classification (MUSIC) algorithm to estimate signal-propagation path parameters, such as angle of arrival (AoA) and Doppler shift, in a low signal-to-noise ratio (SNR) ship environment. We then developed CSI-velocity and activity models based on fine-grained signal parameters, using wavelet transforms and deep learning techniques to determine bridge-officer activities. Finally, we developed a watchkeeping vigilance evaluation module to determine whether a bridge officer was complying with safe driving guidelines during their watchkeeping. Our proposed system was implemented on a commercial Wi-Fi platform, and we extensively evaluated the system on an actual passenger ship. Our proposed system achieved accuracies of 95.4% and 93.8% for tracking walking movements and recognizing careless activities, respectively.