Real-time health status monitoring system based on a fuzzy agent model

This paper presents an Intelligent System which facilitates real-time remote patient's health status monitoring based on the Intelligent Agents and Fuzzy Logic approach. While using the proposed system, patients can stay in the comfort of their own homes and perform daily living activities, while the system "silently monitors" his/her health condition, detects irregularities, and alerts medical personnel in case of emergency situations. The Fuzzy Decision Agent uses the Fuzzy Inference System (FIS) developed for this specific case in order to classify the patient's health status based on the fusion of physiological measurements collected from the patient's body. Using the warning score system for each monitored vital sign, this agent recognized abnormal situations, and access external services in order to alert physicians or emergency services if major changes occur in the patient's health condition. The results of the patient's health status classification were examined and compared by two experts.

[1]  Michael Wooldridge,et al.  An Introduction to MultiAgent Systems, Second Edition , 2009 .

[2]  Oana Geman.  Nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier for automatic assessing of tremor severity , 2013, 2013 E-Health and Bioengineering Conference (EHB).

[3]  Chuan-Jun Su,et al.  JADE implemented mobile multi-agent based, distributed information platform for pervasive health care monitoring , 2011, Appl. Soft Comput..

[4]  Anthony J. Maeder,et al.  Using Fuzzy Logic for Decision Support in Vital Signs Monitoring , 2013, AIH+CARE@AUS-AI.

[5]  C. Subbe,et al.  Validation of a modified Early Warning Score in medical admissions. , 2001, QJM : monthly journal of the Association of Physicians.

[6]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[7]  Mirza Mansoor Baig,et al.  mHealth Monitoring System for Hospitalised Older Adults – Current Issues and Challenges , 2015 .

[8]  Mohamed Achraf Dhouib,et al.  A Fuzzy Logic Approach for Remote Healthcare Monitoring by Learning and Recognizing Human Activities of Daily Living , 2012 .

[9]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[10]  Aleksandar Milenkovic,et al.  Journal of Neuroengineering and Rehabilitation Open Access a Wireless Body Area Network of Intelligent Motion Sensors for Computer Assisted Physical Rehabilitation , 2005 .

[11]  Alessandro Copetti,et al.  A decision-making mechanism for context inference in pervasive healthcare environments , 2013, Decis. Support Syst..

[12]  Oana Geman,et al.  Parkinson's disease Assessment using Fuzzy Expert System and Nonlinear Dynamics , 2013 .

[13]  Oana Geman,et al.  A fuzzy expert systems design for diagnosis of Parkinson's disease , 2011, 2011 E-Health and Bioengineering Conference (EHB).

[14]  R.M. Sperb,et al.  Fuzzy Agent-based Model: A hybrid tool for exploring spatial perception and behavior , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[15]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[16]  H. Costin,et al.  Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier , 2014 .