Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing

Area of Science:

• Computer Science
• Biomedical Engineering
• Digital Health

Background:

• Mobile sensing leverages phone and wearable sensors for health insights.
• Current platforms lack flexibility for custom studies and data standardization.
• Proprietary data formats hinder data comparison and reproducibility.

Purpose of the Study:

• To present software architecture patterns for extensible mobile sensing.
• To enable dynamic incorporation of new sensing capabilities.
• To facilitate real-time data transformation into standardized formats.

Main Methods:

• Developed two software architecture patterns for mobile sensing.
• Implemented patterns in the CARP Mobile Sensing (CAMS) cross-platform architecture.
• Integrated support for electrocardiography (ECG) devices and Open mHealth (OMH) data format.

Main Results:

• Demonstrated dynamic extension for new sensor data collection.
• Showcased real-time data transformation into standardized formats.
• Validated CAMS robustness and feasibility in a pilot study.

Conclusions:

• The proposed patterns enhance mobile sensing flexibility and adaptability.
• Standardized data formats improve data comparability and reproducibility.
• CAMS offers a robust solution for mobile health data collection and transformation.