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Wireless behavioral and environmental monitoring for healthcare

The environmental impacts of our daily activities are largely invisible to us - carbon dioxide from our cars, fertilizers from our lawns, environmental noise and human stress from driving - yet the impact on our long-term health is inevitable. By pervasively monitoring ourselves and our immediate environs, aggregating the data for analysis, and reflecting the results back to us quickly, we can avoid toxic locales, appreciate the consequences of our individual behaviors, and together seek a mandate for change. Today, the infrastructure of our regulatory institutions is inadequate for the cause: sensors are few, often far from where we live, and the results are slow to come to us. What about the air quality on your jogging route or commute? Can you be told when it matters most?

The Internet of Things (IoT) refers to an environment of ubiquitous sensing and actuation, where devices are connected to a distributed backend infrastructure. It offers the opportunity to access a large amount of input data and process it into contextual information about different system entities for reasoning and actuation. State-of-the-art IoT applications are generally black-box, end-to-end application-specific implementations, and cannot keep up with timely resolution of all this live, continually updated, heterogeneous data. We propose a modular approach to these context-aware applications, breaking down monolithic applications into an equivalent set of functional units. By exploiting the characteristics of context-aware applications, the smaller functional units can reduce computational redundancy and complexity. In conjunction with formal data specifications, or ontologies, we can replace application-specific implementations with a composition of common statistical learning to generate the same output.

System Overview

To address this opportunity and the ensuing challenges, UC San Diego has assembled an interdisciplinary team that includes software engineering, embedded systems, AI, security and cryptography, ubiquitous computing, and preventive medicine, as well as leveraging expertise and resources from local companies and across the UCSD campus. We propose that aspect-oriented extensions to a publish-subscribe architecture, comprising the Open Rich Services architecture (ORS), can provide a highly extensible and adaptive infrastructure. As just one example, ORS will enable highly adaptive power management that not only adapts to current device conditions, but also the nature of the data, the data's application, and the presence and status of other sensors in the area. In this way, ORS and its application to CitiSense will enable research advances in power management and the other research areas of this proposal. A CitiSense test-bed and user studies will enable in-the-world experiments and validation of the research.

The second phase of this project addresses the calibration of personal air quality sensors in the field. A significant barrier to ubiquitous health monitoring is the high cost of sensor calibration and the limited confidence that physicians can have in the data collected by inexpensive sensors. We are leveraging large networks of mobile sensors to support self-calibration. The cloud enables using large data repositories and intelligent computational power to cross-reference data from different sensors to detect loss of calibration. The individual boards must be able to discern gradual loss of calibration by discerning signal from noise, negotiating cross-contaminants and identifying sensor dependencies to humidity, temperature and barometric pressure. Using machine learning on contextual data, we aim to permit large-scale cross-device calibration. This involves solvering numerous problems across several disciplines.

Project phases:
  1. CitiSense - Adaptive Services for Community-Driven Behavioral and Environmental Monitoring to Induce Change
  2. MetaSense - Calibration of Personal Air Quality Sensors in the Field – Coping with Noise and Extending Capabilities

For more information, see the CitiSense website.