A Low-Cost Mobile Adaptive Tracking System for Chronic Pulmonary Patients in Home Environment

Isik A. H., GÜLER İ., Sener M. U.

TELEMEDICINE AND E-HEALTH, vol.19, no.1, pp.24-30, 2013 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 1
  • Publication Date: 2013
  • Doi Number: 10.1089/tmj.2012.0056
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.24-30
  • Keywords: Bluetooth, extensible markup language, artificial neural network, asthma, chronic obstructive pulmonary disease, DECISION-SUPPORT ARCHITECTURE, MANAGEMENT
  • Gazi University Affiliated: Yes


Objective: The main objective of this study is presenting a real-time mobile adaptive tracking system for patients diagnosed with diseases such as asthma or chronic obstructive pulmonary disease and application results at home. The main role of the system is to support and track chronic pulmonary patients in real time who are comfortable in their home environment. It is not intended to replace the doctor, regular treatment, and diagnosis. Materials and Methods: In this study, the Java 2 micro edition-based system is integrated with portable spirometry, smartphone, extensible markup language-based Web services, Web server, and Web pages for visualizing pulmonary function test results. The Bluetooth (R) (Bluetooth SIG, Kirkland, WA) virtual serial port protocol is used to obtain the test results from spirometry. General packet radio service, wireless local area network, or third-generation-based wireless networks are used to send the test results from a smartphone to the remote database. The system provides real-time classification of test results with the back propagation artificial neural network algorithm on a mobile smartphone. It also provides the generation of appropriate short message service-based notification and sending of all data to the Web server. In this study, the test results of 486 patients, obtained from Ataturk Chest Diseases and Thoracic Surgery Training and Research Hospital in Ankara, Turkey, are used as the training and test set in the algorithm. Results: The algorithm has 98.7% accuracy, 97.83% specificity, 97.63% sensitivity, and 0.946 correlation values. The results show that the system is cheap (900 Euros) and reliable. Conclusions: The developed real-time system provides improvement in classification accuracy and facilitates tracking of chronic pulmonary patients.