IEEE 802.15.4 Wireless Mobile Application for Healthcare System

The new generation of technology that focuses on integrating of existing technologies such as wireless technology and mobile network has great potential to create a well-established quality, wide coverage range, high mobility and enhanced QoS heterogeneous for wireless computing system/application. To go beyond technology, services provided have to be adapted to the users needs and enable access to information in a more flexible manner. With the right implementation, wireless mobile computing devices can be integrated into the healthcare environment. Wireless and mobility transforms the way medical facility functions and deliver the healthcare to patients. This paper describes the developed wireless mobile healthcare application that leverages the wireless capabilities, transforms the medical information and delivers the healthcare to patients. This system is capable to support the real time application to gather and transmits the medical data regardless of the physical location among IEEE802.15.4 wireless network and CDMA cellular network for hospital and home environments.

[1]  Chien-Chung Chan,et al.  A Rough Set Approach to Attribute Generalization in Data Mining , 1998, Inf. Sci..

[2]  R. Yager.  Families of OWA operators , 1993 .

[3]  Shao-Shan Chiang,et al.  Design and Implementation of a Mobile-Care System over Wireless Sensor Network for Home Healthcare Applications , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[5]  R. Yager,et al.  PARAMETERIZED AND-UKE AND OR-LIKE OWA OPERATORS , 1994 .

[6]  F. Gagné,et al.  Application of rough sets analysis to identify polluted aquatic sites based on a battery of biomarkers: a comparison with classical methods. , 2003, Chemosphere.

[7]  Qiang Shen,et al.  Centre for Intelligent Systems and Their Applications Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Sets and Systems ( ) – Fuzzy–rough Attribute Reduction with Application to Web Categorization , 2022 .

[8]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[9]  Ronald R. Yager,et al.  Applications and Extensions of OWA Aggregations , 1992, Int. J. Man Mach. Stud..

[10]  Robert Fullér,et al.  An Analytic Approach for Obtaining Maximal Entropy Owa Operator Weights , 2001, Fuzzy Sets Syst..

[11]  Han Tong Loh,et al.  Applying rough sets to market timing decisions , 2004, Decis. Support Syst..

[12]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[13]  M. Matsumoto,et al.  A simulation based evaluation on the performance of integrated 3G wireless LAN network , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[14]  Gordon Anderson,et al.  Nonparametric Tests of Stochastic Dominance in Income Distributions , 1996 .

[15]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[16]  Zdzisław Pawlak,et al.  Rough sets applied to the discovery of materials knowledge , 1998 .

[17]  Sankar K. Pal,et al.  Evolutionary Modular MLP with Rough Sets and ID3 Algorithm for Staging of Cervical Cancer , 2001, Neural Computing & Applications.

[18]  Marko Robnik-Sikonja,et al.  Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.

[19]  Mosa Ali Abu-Rgheff,et al.  3G wireless communications for mobile robotic tele-ultrasonography systems , 2006, IEEE Communications Magazine.

[20]  Sankar K. Pal,et al.  Multispectral image segmentation using the rough-set-initialized EM algorithm , 2002, IEEE Trans. Geosci. Remote. Sens..

[21]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[22]  Qiang Shen,et al.  Rough set-aided keyword reduction for text categorization , 2001, Appl. Artif. Intell..

[23]  Ni-Bin Chang,et al.  Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment. , 2003, Water research.

[24]  Lixiang Shen,et al.  Fault diagnosis based on Rough Set Theory , 2003 .

[25]  Mevlut Ture,et al.  Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease , 2008, Expert Syst. Appl..