The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants’ TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults’ fall risks remotely through the evaluation of the TUG score during their daily walking.

Due to a ship’s extreme motion, there is a risk of injuries and accidents as people may become unbalanced and be injured or fall from the ship. Thus, individuals must adjust their movements when walking in an unstable environment to avoid falling or losing balance. A person’s ability to control their center of mass (COM) during lateral motion is critical to maintaining balance when walking. Dynamic balancing is also crucial to maintain stability while walking. The margin of stability (MOS) is used to define this dynamic balancing. This study aimed to develop a model for predicting balance control and stability in walking on ships by estimating the peak COM excursion and MOS variability using accelerometers. We recruited 30 healthy individuals for this study. During the experiment, participants walked for two minutes at self-selected speeds, and we used a computer-assisted rehabilitation environment (CAREN) system to simulate the roll motion. The proposed prediction models in this study successfully predicted the peak COM excursion and MOS variability. This study may be used to protect and save seafarers or passengers by assessing the risk of balance loss.

Walking strategies in an unstable environment like a ship differ from walking on stable ground. Extreme ship motions may endanger the safety of the crews. Notably, a loss of balance on board can lead to an injury or an accident of falling off a ship. Keeping one’s balance on board a ship is strongly influenced by the ship’s motion. Therefore, the objective of this study is to determine how walking on a ship differs from walking in a stable environment and explore the effects of the ship’s roll motion on balance control and stability while walking in sea environments. We hypothesized that step time variability, center of mass (COM), and margin of stability (MOS) would significantly differ between stable and unstable walking conditions. We also hypothesized that there would be an effect of rolling cycles and angles on increasing step time variability, COM excursion, and MOS variability. We recruited 30 healthy individuals between 21 and 39 years old for this study. Participants walked for two minutes at their self-selected speeds during the study with and without rolling on a computer-assisted rehabilitation environment (CAREN) system. The CAREN system was used to simulate the parametric roll motion of ships up to 20 degrees. This study quantified step time variability, peak COM excursion, and MOS variability in different rolling conditions. We found a significant difference in step time variability (p < 0.001), lateral peak COM excursion (p < 0.001), and MOS variability (p < 0.001) between waking on land and walking at sea.

Ferry terminals are an essential facility for those frequently commuting between islands or towns ashore. Therefore, it is crucial to ensure a smooth and efficient flow of passengers and vehicles while guaranteeing safety and convenience at the ferry terminal. This study investigates and evaluates the walking path environment and determines the passengers’ walkability and walking satisfaction of ferry terminals in Korea. As a case study, to measure the passenger’s overall perception and satisfaction of the built environment of the ferry terminal, we conducted an importance–performance analysis for two ferry terminals located in Mokpo city of Korea. The segments of the poor built environment in terms of walking were found. Furthermore, the ANOVA and t-test results confirmed that the satisfaction level of the built environment varied by age and residential location of passengers. There was a significant difference in satisfaction between the groups (age and residential location) in the walking path segments while embarking and disembarking the ferry. Passengers’ perceptions and walking satisfaction were different depending on the features of the built environment, including public transport accessibility, layout, distance, and surface condition of the walking path of the ferry terminal. As a limitation of the study, the case study was conducted only in the Mokpo region due to the impact of COVID-19, and the sample survey was also conducted in a short period of time. In addition, further studies are needed on the generalization of passengers’ walkability in ferry terminals.

Falls on a ship cause severe injuries, and an accident falling off board, referred to as “man overboard” (MOB), can lead to death. 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 computer-assisted 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). Logistic regression showed the best performance in terms of the AUC for both training (0.9483) and testing (0.9204). We anticipate that this study will effectively help identify the risk of falls on ships and aid in developing a monitoring system capable of averting falls and detecting MOB situations.

Despite the development of advanced technology, marine accidents have not decreased. To prevent marine accidents, it is necessary to predict accidents in advance. With the recent development of artificial intelligence (AI), AI technologies such as deep learning have been applied to create and analyze predictive models in various fields. The purpose of this study is to develop a model for predicting the frequency of marine accidents using a long-short term memory (LSTM) network. In this study, a prediction model was developed using marine accidents from 1981 to 2019, and the proposed model was evaluated by predicting the accidents in 2020. As a result, we found that marine accidents mainly occurred during the third officer’s duty time, representing that the accidents are highly related to the navigator’s experience. In addition, the proposed LSTM model performed reliably to predict the frequency of marine accidents with a small mean absolute percentage error (best MAPE: 0.059) that outperformed a traditional statistical method (i.e, ARIMA). This study could help us build LSTM structures for marine accident prediction and could be used as primary data to prevent the accidents by predicting the number of marine accidents by the navigator’s watch duty time.