Wireless health monitoring of multiple patients on android phone with embedded computation


PINARER Ö. , Parmaksiz A., Avci I., Arslan B. , Ozgovde A.

9th IASTED International Conference on Biomedical Engineering, BioMed 2012, Innsbruck, Austria, 15 - 17 February 2012, pp.569-573 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.2316/p.2012.764-153
  • City: Innsbruck
  • Country: Austria
  • Page Numbers: pp.569-573
  • Keywords: Ambient Assisted Living, Biomedical Computing, Health Care Information System, Mobile Health Health

Abstract

In recent years, there has been a significant increase in the number and variety of application scenarios studied under the m-health (mobile health) domain. While this multitude of approaches enrich m-health based solutions, lack of widely accepted tools and platforms for their evaluation makes it difficult to draw generally applicable conclusions from the current research efforts. Suitable platforms are needed to test the validity of the scenarios where they can be run under similar settings. In this study, an open system platform that enables fast prototyping of m-health applications is proposed. Typically m-health based schemes are dispersed over a broad spectrum of scenarios ranging from domestic body weight control to monitoring of vital signals for chronic diseases. The platform proposed is composed of all the necessary hardware and software components to implement virtually any mobile health application scenario and conduct measurement based experiments. To enable reproducibility of the results by the research community, the components are based on open systems. The platform is capable of taking real-time measurements from multiple patients simultaneously and performs differentiated processing based on signal type and/or patient identity. Wirelessly transmitted signals are captured by an Android smart phone that acts as the local information processing center. To evaluate the usability of our platform we deployed a sample ECG based m-health scenario where onboard processing is used to reduce the data traffic from sensor nodes to the local center, hence enhance the battery life of the nodes.