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

Mobile sensing-that is, the ability to unobtrusively collect sensor data from built-in phone and attached wearable sensors-have proven to be a powerful approach to understanding the behavior, well-being, and health of people in their everyday life. Different platforms for mobile sensing have been presented and significant knowledge on how to facilitate mobile sensing has been accumulated. However, most existing mobile sensing platforms only support a fixed set of mobile phone and wearable sensors which are `built into' the platform's generic `study app'. This creates some fundamental challenges for the creation and approval of application-specific mobile sensing studies, since there is little support for adapting the sensing capabilities to what is needed for a specific study. Moreover, most existing platforms use their own proprietary data formats and there is no standardization in how data are collected and in what formats. This poses some fundamental challenges to realizing the vision of using mobile sensing in health applications, since mobile sensing data collected across different phones and studies cannot be compared, thus hampering generalizability and reproducibility across studies. This paper presents two software architecture patterns enabling (i) dynamic extension of mobile sensing to incorporate new sensing capabilities, such as collecting data from a wearable sensor, and (ii) handling real-time transformation of data into standardized data formats. These software patterns are derived from our work on CARP Mobile Sensing (CAMS), which is a cross-platform (Android/iOS) software architecture providing a reactive and unified programming model that emphasizes extensibility. This paper shows how the framework uses the two software architecture patterns to add sampling support for an electrocardiography (ECG) device and support data transformation into the new Open mHealth (OMH) data format. The paper also presents data from a small study, demonstrating the robustness and feasibility of using CAMS for data collection and transformation in mobile sensing.