Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective

Traditional pain assessment approaches ranging from self-reporting methods, to observational scales, rely on the ability of an individual to accurately assess and successfully report observed or experienced pain episodes. Automatic pain assessment tools are therefore more than desirable in cases where this specific ability is negatively affected by various psycho-physiological dispositions, as well as distinct physical traits such as in the case of professional athletes, who usually have a higher pain tolerance as regular individuals. Hence, several approaches have been proposed during the past decades for the implementation of an autonomous and effective pain assessment system. These approaches range from more conventional supervised and semi-supervised learning techniques applied on a set of carefully hand-designed feature representations, to deep neural networks applied on preprocessed signals. Some of the most prominent advantages of deep neural networks are the ability to automatically learn relevant features, as well as the inherent adaptability of trained deep neural networks to related inference tasks. Yet, some significant drawbacks such as requiring large amounts of data to train deep models and over-fitting remain. Both of these problems are especially relevant in pain intensity assessment, where labeled data is scarce and generalization is of utmost importance. In the following work we address these shortcomings by introducing several novel multi-modal deep learning approaches (characterized by specific supervised, as well as self-supervised learning techniques) for the assessment of pain intensity based on measurable bio-physiological data. While the proposed supervised deep learning approach is able to attain state-of-the-art inference performances, our self-supervised approach is able to significantly improve the data efficiency of the proposed architecture by automatically generating physiological data and simultaneously performing a fine-tuning of the architecture, which has been previously trained on a significantly smaller amount of data.

A method for assessing of ship fuel system failures resulting from fuel changeover imposed by environmental requirements

Environmental regulations instigated the technological and procedural revolution in shipping. One of the challenges has been sulfur emission control areas (SECA) and requirement of fuel changeover. Initially, many reports anticipated that new grades of low sulfur fuels might increase various technical problems in ship operation. This research develops a simple and easy to use method of the failure severity and intensity assessment in relation to fuel changeover. The scale of failure rate in the ship’s fuel system was evaluated qualitatively and quantitively, using developed failure frequency indicator and the time between failure. Based on 77 records of fuel system failures collected on seven ships, it has been found that frequency of failures related to SECA fuel changeover is on average nearly three times higher compared to the rest of sailing time. Their severity did not significantly change, but the structure of failures changed considerably. The method and presented results may help in improvement of ship’s systems design and on-board operational procedures.