Science of perception, decision making and fatigue in the maritime industry

Wellbeing and Happiness and Their Association With Working Conditions at Sea: A Cross-sectional Study Among the Global Workforce of Seafarers

The aim of this study was to investigate whether seafarers' self-reported work experiences were associated with wellbeing and happiness while onboard. The study also examined which indicators of the work experiences had an effect in what direction. We analyzed the survey responses from 13 008 seafarers onboard, from 154 different nationalities, serving in 44 different international shipping companies. The outcome measures were wellbeing and happiness, and the exposure variables were work environment factors: satisfaction, expectations, ideal, skills and training, challenges, and workload. General psychosocial work environment onboard and socioeconomic independent variables were also included. We conducted different logistic regression analyses, and presented the results as odds ratios (OR) and 95% confidence intervals (CIs). The study found that most seafarers reported high levels of wellbeing and happiness and that these were significantly associated to the work environment factors, except for workload. A stratified analysis, showed that workload modified the effect of the other work environment factors. The study found that there were independently significant associations between work related factors and wellbeing and happiness among seafarers at sea. The findings suggest that a greater emphasis on these outcomes could have a positive impact both on crew retention and safety at sea.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.