Man Overboard Detection System Using IoT for Navigation Model

Abstract

Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry, roro, catamaran, frigate, yacht that are the vehicles services for the purpose of logistic and passenger transport. In this paper, all processes in the event of Man overboard (MOB)are initiated for smart transportation. In MOB the falling person is totally dependent on the person who first saw the falling person. The main objective of this paper is to develop a solution to this significant problem. If a staff member or a passenger does not see the fall into the sea, undesirable situations such as disappearance, injury and death can occur during the period until the absence of the fallen person is noticed. In this paper, a comprehensive and improved solution is provided in terms of personnel and passenger security especially in all the floating elements, in which human resources are intensively involved like passengers, freight, logistics, fishing, business, yacht, leisure and naval vessels. In this case, if the ship's personnel or passengers fall into the sea in any way, it detected the fallen person into the sea by the sensors in the portable emergency device, which each person will carry. The warning system is activated via the in-ship automation system to which the information is transmitted by wireless communication. Thus, the case of MOB will be determined quickly. Internet of things (IoT) has a key role in identifying the location and information of the person falling into the sea through sensors, radio frequency, GPS and connected devices. Simultaneously, the alarm system on board will be activated and MOB flag (Oscar) will automatically be opened. This paper enables the Search and rescue (SAR) operations to be initiated and accelerated without losing time through decision-making process.

Machine Learning-Based Approach to Identifying Fall Risk in Seafarers Using Wearable Sensors

“…Many researchers have studied the assessment of fall risk [11][12][13][14], but most of them examined older adults, and little research has been conducted on fall risk identification for seafarers on a ship. In addition, existing studies related to classifying fall risks have been undertaken via different devices like radars and cameras [15][16][17][18][19]. However, wearable sensor-based fall risk evaluation studies have been relatively insufficient.…”

“…There are fewer studies on the prediction or detection of seafarers' falls in the maritime field. Only a few previous studies have attempted to detect MOBs through ML technologies [15][16][17][18][19]. Tsekenis et al [15] implemented an ML-based system to detect MOBs using radar sensors.…”

“…They emphasized that the system can detect and track real human fall scenarios. Gürüler et al [19] designed an MOB detection system module using GPS, radio frequency, and a mobile ad hoc network to warn of an MOB situation, including the location and information of the individual involved in the MOB. However, these studies need expensive devices, such as radar sensors and video cameras, and some challenges still exist.…”

LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System

“…With the vigorous development of satellite navigation technology, researchers integrate satellite navigation technology into the research of PIBS [8][9][10][11]. Huang designed a lifesaving terminal based on BeiDou positioning and BeiDou short message technology [12].…”