Abstract
Investigators discuss new findings in artificial intelligence. According to news reporting originating from Gwangju, South Korea, by NewsRx correspondents, research stated, “Falls on a ship cause severe injuries, and an accident falling off board, referred to as “man overboard” (MOB), can lead to death.” Financial supporters for this research include Ministry of Education; Office of Research And Creative Activity (Orca) of The University of Nebraska At Omaha. Our news reporters obtained a quote from the research from Chosun University: “Thus, it is crucial to accurately and timely detect the risk of falling. Wearable sensors, unlike camera and radar sensors, are affordable and easily accessible regardless of the weather conditions. This study aimed to identify the fall risk level (i.e., high and low risk) among individuals on board using wearable sensors. We collected walking data from accelerometers during the experiment by simulating the ship’s rolling motions using a computerassisted rehabilitation environment (CAREN). With the best features selected by LASSO, eight machine learning (ML) models were implemented with a synthetic minority oversampling technique (SMOTE) and the best-tuned hyperparameters. In all ML models, the performance in classifying fall risk showed overall a good accuracy (0.7778 to 0.8519), sensitivity (0.7556 to 0.8667), specificity (0.7778 to 0.8889), and AUC (0.7673 to 0.9204).”
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