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The Internet of Things (IoT) envisions to create a smart, connected city that is composed of ubiquitous environmental and user sensing along with distributed, low-capacity computing. This provides ample information regarding the citizens in various smart environments. We can leverage this people-centric information, provided by the smart city infrastructure, to improve “smart health” applications: user data from connected wearable devices can be accompanied with ubiquitous environmental sensing and versatile actuation. The state-of-the-art in smart health applications is black-box, end-to-end implementations which are neither intended for use with heterogeneous data nor adaptable to a changing set of sensing and actuation. In this paper, we apply our modular approach for IoT applications - the context engine - to smart health problems, enabling the ability to grow with available data, use general-purpose machine learning, and reduce compute redundancy and complexity. For smart health, this improves response times for critical situations, more efficient identification of health-related conditions and subsequent actuation in a smart city environment. We demonstrate the potential with three sets of interconnected context-aware applications, extracting health-related people-centric context, such as user presence, user activity, air quality, and location from IoT sensors.
Venkatesh et al. (Tue,) studied this question.