Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology

Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely.

Accidents, diseases and health complaints among seafarers on German-flagged container ships

BACKGROUND

For seafarers on the high seas health hazards are various and due to the setting also specific. The spectrum of job-related health impairments and accidents is mainly influenced by the maritime characteristics. The aim of this study is to assess the kind of accidents and the frequency of diseases and health complaints among seafarers on German container ships by evaluating medical log books.

METHODS

A systematic analysis of 14,628 medical entries from 95 medical log books of 58 container ships under German flag from 1995 to 2015 was performed. This monocentric retrospective and descriptive study used information on accidents, diseases and health complaints among different occupational groups and medical treatment procedures for the analysis and evaluation.

RESULTS

The analysis showed that more than one third of all consultations with the Health Officer on board are related to internal (33.7%) and surgical (31.3%) symptoms. Almost twenty percent of consultations were due to respiratory infections (19.6%) and accidents (17.9%). Accidents represented the most frequent reason for unfitness for sea service (31.2%). Based on occupational categories, most injuries occurred among deck crew (22.5%), followed by ratings working in the engine room (18.9%). In 106 cases, telemedical contact with a physician ashore was necessary. In total, 15 seafarers had to be evacuated from the ship for further medical treatment onshore. Medicine/drug application was the most common therapeutic measure on board, accounting for 77% of all consultations.

CONCLUSIONS

The high proportion of health complaints and accidents among seafarers shows that there is a need to optimize medical care at sea and accident prevention, e.g. by standardized treatment algorithms or improving the medical training of Health Officers. The development and introduction of a digital patient file to record medical treatments on the vessels could also improve medical documentation on board.