The Future of Mobile E-Health Application Development Exploring HTML5 for Context-aware Diabetes Monitoring Speaker: Nishant Chettri

Introduction  Context aware mobile application assisting with self health care  Digitize manual labor  Analyze captured data  Provide personalized assistance  Context aware mobile application assisting with self health care  Digitize manual labor  Analyze captured data  Provide personalized assistance

Problem  Multivariate data input (date, time, glucose level, etc.)  Lack of good data visualization for decision support  Cross-platform deployment  Cost effective  Multivariate data input (date, time, glucose level, etc.)  Lack of good data visualization for decision support  Cross-platform deployment  Cost effective

Identified Solution  Gartner predicts HTML5 web applications as the future  Cross platform development tools  Phonegap  CSS  Javascript  jQuery UI  Gartner predicts HTML5 web applications as the future  Cross platform development tools  Phonegap  CSS  Javascript  jQuery UI

Motivation  Context aware cross platform implementation  Mobile  Desktop  Proper data visualization for decision support  Context aware cross platform implementation  Mobile  Desktop  Proper data visualization for decision support

Data Visualization

Related Work  Feasibility exploration on HTML5 for Mobile (Juntunen et al.)  Performance and Cost/Cross-platform trade off  Security problems in HTML5 (De Ryck et al.)  Web Storage privacy, security and performance concerns (West et al.)  Feasibility exploration on HTML5 for Mobile (Juntunen et al.)  Performance and Cost/Cross-platform trade off  Security problems in HTML5 (De Ryck et al.)  Web Storage privacy, security and performance concerns (West et al.)

Context Awareness  Time and Location awareness  Activity Recognition  Tri-Axial Accelerometer  Low pass filter: 5Hz  Magnitude Filter: Orientation  Peak Filter: Maxima / Minima  Step Detector  High-pass filter and Signal Magnitude Area Filter (Fall Detection)  Time and Location awareness  Activity Recognition  Tri-Axial Accelerometer  Low pass filter: 5Hz  Magnitude Filter: Orientation  Peak Filter: Maxima / Minima  Step Detector  High-pass filter and Signal Magnitude Area Filter (Fall Detection)

HTML5 Approach  Time and Location: W3C Geolocation API Specification  Accelerometer access requires Hybrid approach [HTML5 + Native Container]  Visualization of data for Decision Support  Parallel Coordinates  D3.js (Data Driven Documents)  Time and Location: W3C Geolocation API Specification  Accelerometer access requires Hybrid approach [HTML5 + Native Container]  Visualization of data for Decision Support  Parallel Coordinates  D3.js (Data Driven Documents)

Data Visualization with Parallel Coordinates

Feasibility Analysis  Devices:  Mobile: ASUS Android Tablet with Chrome and Firefox web browsers  Desktop: Chrome and Firefox web browsers  Offline Use  Performance Evaluation  Activity Recognition  Encryption  User Experience and Data Visualization  Devices:  Mobile: ASUS Android Tablet with Chrome and Firefox web browsers  Desktop: Chrome and Firefox web browsers  Offline Use  Performance Evaluation  Activity Recognition  Encryption  User Experience and Data Visualization

Offline Use  Persistent HTML5 Web storage  Local storage and Session storage  Similar to cookies (Key-Value pair)  Limited to 5 MB  Unencrypted  Lack of storage alternatives  Persistent HTML5 Web storage  Local storage and Session storage  Similar to cookies (Key-Value pair)  Limited to 5 MB  Unencrypted  Lack of storage alternatives

Performance Evaluation  Activity Recognition  Negligible for simple context processing  High CPU load drains battery  Activity Recognition  Negligible for simple context processing  High CPU load drains battery

Performance Evaluation  Encryption  AES and PBKDF2  JSON data store  Full pseudo-database encryption  Encryption  AES and PBKDF2  JSON data store  Full pseudo-database encryption

User experience and Data Visualization  Additional Devices: Samsung Galaxy S3/S4 and Google Nexus 4  High relative error in sampling frequency  Loss in data accuracy  Data visualization powerful, requires larger displays  Web browsers smoother than Phonegap  Additional Devices: Samsung Galaxy S3/S4 and Google Nexus 4  High relative error in sampling frequency  Loss in data accuracy  Data visualization powerful, requires larger displays  Web browsers smoother than Phonegap

Conclusion  Benefits of using HTML5 and related web technology  Reduced development across multiple platforms  Feasible for simpler applications  Limitations  Encryption and user experience concerns  Offline data storage  Benefits of using HTML5 and related web technology  Reduced development across multiple platforms  Feasible for simpler applications  Limitations  Encryption and user experience concerns  Offline data storage

Future  HTML5 Specification addresses concerns  Mitigate performance cost by utilizing the cloud  HTML5 Specification addresses concerns  Mitigate performance cost by utilizing the cloud

Questions?