Department of Health Technology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark.
Sensors (Basel). 2022 Apr 6;22(7):2813. doi: 10.3390/s22072813.
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.
移动感应，即能够从内置手机和附加可穿戴传感器中悄无声息地收集传感器数据，已被证明是一种了解人们日常生活中的行为、健康和幸福感的强大方法。已经提出了不同的移动感应平台，并积累了大量关于如何促进移动感应的知识。然而，大多数现有的移动感应平台仅支持固定的一组手机和可穿戴传感器，这些传感器被“内置”到平台的通用“研究应用程序”中。这为创建和批准特定于应用的移动感应研究带来了一些基本挑战，因为几乎没有支持根据特定研究的需要来调整感应功能。此外，大多数现有的平台都使用自己专有的数据格式，并且在数据收集和使用什么格式方面没有标准化。这对实现使用移动感应在健康应用中的愿景提出了一些基本挑战，因为无法比较来自不同手机和研究的移动感应数据，从而阻碍了研究之间的通用性和可重复性。本文提出了两种软件架构模式，能够(i)动态扩展移动感应以纳入新的感应功能，例如从可穿戴传感器收集数据，以及(ii)处理实时将数据转换为标准化数据格式。这些软件模式源自我们在 CARP 移动感应（CAMS）方面的工作，CAMS 是一个跨平台（Android/iOS）软件架构，提供了一个反应式和统一的编程模型，强调可扩展性。本文展示了框架如何使用这两种软件架构模式为心电图（ECG）设备添加采样支持，并支持将数据转换为新的 Open mHealth（OMH）数据格式。本文还展示了一项小型研究的数据，证明了使用 CAMS 进行移动感应中的数据收集和转换的稳健性和可行性。
