Open Sensing Smartphone Framework for
Diabetes Management
Andrew Ziang Chen

Supervisor: Jinman Kim

School of Information...
Upcoming SlideShare
Loading in …5
×

Open sensing smartphone framework for diabetes management. Andrew Chen, Faculty of Engineering and IT

217 views

Published on

Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
217
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Open sensing smartphone framework for diabetes management. Andrew Chen, Faculty of Engineering and IT

  1. 1. Open Sensing Smartphone Framework for Diabetes Management Andrew Ziang Chen Supervisor: Jinman Kim School of Information Technologies FACULTY OF ENGINEERING & INFORMATION TECHNOLOGIES MOTIVATION METHODS • Diabetes affects millions of people worldwide Data input • Traditional methods of management that rely on manual input of data are inefficient • Smartphones are widely available and they have the capabilities to make diabetes management more effective • By utilising built-in functionalities such as access to sensors and other data • As well as their capability of accessing and processing data from anywhere and have social connectivity • Automatic collection • FitBit activity tracker • Exercise information • Sleep monitoring • Smartphone sensors • Smartphone data • Manual input • HealthVault • Account authorisation allows external access to data collected by framework • Health professionals; family • Applications and devices Data output • Timeline overview of daily average blood glucose trend along with data collected. Navigate data by tapping on graph • Glucose level • Diet (Using FitBit food database) • Additional exercise activities AIMS • Create an open sensing smartphone framework for diabetes management that combines smartphones and external sensors to form a sensory system and collect data applicable to diabetes management • Data analysis and output directly on smartphones to provide guidance information for diabetes management Tools • FitBit • Wearable sensor; multiple form factors • High compatibility API • Extensive food database with nutritional information • Alerts and notifications • Customisable by the user • Glucose check, medication, exercise reminders • High carbohydrate intake alert RESULTS CONCLUSION • Effective collection of data using sensory network • An open sensing framework which captures data automatically from sensors, combined with other data commonly available on smartphones, and outputs essential information for effective diabetes management in the form of a companion smartphone app • Data is processed and stored locally and on the cloud for external access • Information provided to user to assist in the management of diabetes • User is able to use the framework to input a wide range of information to be processed along with the automatically collected data FUTURE WORK • Implement the ability for the framework to recognise trends in the data. • Throughout a day of using the framework, a user will: • Discover correlations between the different data types captured by the framework. • Input blood glucose levels • Record meals eaten • Receive processed information and adjust behaviour accordingly for the future THIS RESEARCH IS SP • Introduce integration with glucose meters to achieve faster and more convenient input of blood glucose levels • Undertake clinical trial to evaluate the effectiveness of the framework

×