Internet of Things. User-Centric IoT. First International Summit, IoT360 2014, Rome, Italy, October 27-28, 2014, Revised Selected Papers, Part I

Research Article

Real-Time Monitoring Using Finite State-Machine Algorithms

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  • @INPROCEEDINGS{10.1007/978-3-319-19656-5_27,
        author={Sebastian Fuicu and Andrei Avramescu and Diana Lascu and Roxana Padurariu and Marius Marcu},
        title={Real-Time Monitoring Using Finite State-Machine Algorithms},
        proceedings={Internet of Things. User-Centric IoT. First International Summit, IoT360 2014, Rome, Italy, October 27-28, 2014, Revised Selected Papers, Part I},
        proceedings_a={IOT360},
        year={2015},
        month={7},
        keywords={Finite state machine algorithms e-health Chronic diseases Patient monitoring Permanent watch Medical complex rules},
        doi={10.1007/978-3-319-19656-5_27}
    }
    
  • Sebastian Fuicu
    Andrei Avramescu
    Diana Lascu
    Roxana Padurariu
    Marius Marcu
    Year: 2015
    Real-Time Monitoring Using Finite State-Machine Algorithms
    IOT360
    Springer
    DOI: 10.1007/978-3-319-19656-5_27
Sebastian Fuicu1,*, Andrei Avramescu1,*, Diana Lascu1,*, Roxana Padurariu1,*, Marius Marcu1,*
  • 1: Politehnica University of Timisoara
*Contact email: sebastian.fuicu@cs.upt.ro, andrei.avramescu@student.upt.ro, diana.lascu@student.upt.ro, roxana.padurari@student.upt.ro, marius.marcu@cs.upt.ro

Abstract

This paper presents the architecture of a medical platform for chronic diseases sufferers that enables specialist physicians to have a permanent overview of the patient’s health. The proposed system, HChecked, integrates the monitoring of vital parameters, reception of notification in case of any exceeding of the pre-defined limits of these parameters and prediction of the evolution of the current disease or of the possibility of occurrence of another disease. The software system follows the idea of trading systems that offers efficient prediction with a high level of security. This concept is based on a particular implementation of finite state-machine algorithms, which enable physicians to run complex rules against particular health information of a certain patient to predict the evolution of the current diseases or the appearance of others. Although the system allows many points of view, this paper is oriented towards the specific way in which complex rules are created.