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Vol. 1 No. 1 (2011): JITA - APEIRON

Mladen Milošević, Michael T. Shrove, Emil Jovanov

Applications of Smartphones for Ubiquitous Health Monitoring and Wellbeing Management

Original scientific paper

DOI: https://doi.org/10.7251/JIT1101007M

Abstract

Advances in smartphone technology and data communications facilitate the use of ubiquitous health monitoring and mobile health application as a solution of choice for the overwhelming problems of the healthcare system. In addition to easier management and seamless access to historical records, ubiquitous technology has the potential to motivate users to take an active role and manage their own conditions.
In this paper we present capabilities of the current generation of smartphones and possible applications for ubiquitous health monitoring and wellness management. We describe the architecture and organization of ubiquitous health monitoring systems, Body Sensor Networks, and integration of wearable and environmental sensors. We also describe mainstream mobile health related applications in today’s mobile marketplaces such as Apple App Store and Google Android Marketplace. Finally, we present the development of UAHealth – our integrated mobile health monitoring system for wellness management, designed to monitor physical activity, weight, and heart activity.

Keywords: Smartphone, Body Sensor Networks, Health, Wellbeing.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.