Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.
In recent years, the reduction of injury crashes has been heralded as a great success. Improvements in federally mandated safety standards and advancements made by automotive industries to enhance vehicle safety can be partially credited with the decline. Now the national strategy on highway safety is to move toward zero deaths. From this vision zero perspective, one of the appropriate strategies is to manage kinetic energy in crashes and collisions—that is, to minimize the energy transferred to the human body—because the kinetic energy is responsible for occupant injuries and fatalities. Vehicle damage conditions are an unbiased indicator of kinetic energy in collisions, and injury severity is the ultimate measure of occupant risk. In this study, vehicle damage and occupant injury models were developed for single-vehicle and multiple-vehicle crashes. The results of these models provide a complete view of crash severity determinants and how they affect occupant injuries and vehicle damage. Some factors have a consistent impact across both injury severity and vehicle damage; others are contradictory. Combining information from both occupants and vehicles is valuable for an impartial evaluation of specific components in highway design; this combining also provides an accurate assessment of the impacts of occupant characteristics, driver behavior, and error on the resulting bodily injuries.
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