PDF(Pubmed)
Abstract：
IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user\'s health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.
摘要：
物联网传感器提供了广泛的传感功能，其中许多都有潜在的健康应用。现有的医疗保健物联网解决方案有明显的局限性，比如闭源，有限的I/O协议，有限的云平台支持，以及缺少健康用例的特定功能。开发开源物联网（IoT）网关解决方案，解决这些限制并提供可靠性，广泛的适用性，和实用性是非常可取的。将来自物联网设备的各种传感器数据流与动态mHealth数据相结合，将为患者生理之间的关系提供详细的360度视图。行为，和环境。我们已经开发了RADAR-IoT作为一个开源的IoT网关框架，利用这种潜力。它旨在连接边缘的多个物联网设备，执行有限的设备上数据处理和分析，并与基于云的移动健康平台集成，比如雷达基地，实现实时数据处理。我们还提供了来自此框架的概念验证数据收集，在两个位置使用原型硬件。RADAR-IoT框架,结合基于雷达的mHealth平台，通过集成静态物联网传感器和可穿戴设备，提供用户健康和环境的全面视图。尽管其目前的局限性，它为健康研究提供了一个有前途的开源解决方案，在管理感染控制方面的潜在应用，监测慢性肺部疾病，帮助运动控制或认知能力受损的患者。
