A new computing paradigm is emerging wherein in-users wear small monitoring devices, especially for medical conditions, that transmit selected information to a local wireless hub either in the home, in the hospital, and potentially in public areas. The collected information is relayed and summarized for medical staff and/or caregivers. The goal of this research is to explore the dimensions of security and privacy in a new data paradigm: real-time monitoring systems. Previous research has shown that small embedded systems in the form of a wrist watch incorporating accelerometers and sensors for light, sound and temperature can, with more than 90% accuracy, determine activity using accelerometers (standing, sitting, walking, running, driving, eating, etc.) and location using light and sound fingerprints.
In our research, we will take machine learning algorithms that we've developed for activity, location identification and "exceptional" behavior identification and deploy them for two populations: mobile workers and people with disabilities including the elderly. The target application will be monitoring feedback, and encouragement for the users to comply with goals set by a clinician or care-giver. We will explore the paradigm where sensors on mobile devices collect and analyze data to determine user activities. Either the user or a clinician, if the user is under medical guidance, will author activity goals that will be downloaded into the mobile device. The mobile device will monitor for compliance and give feedback to the user. In addition, summaries and notifications will be provided to the clinician or caregiver. All these communications should be secure. In additional the user should have control over what information is given to whom. Security and privacy should be over burdening on either the mobile device or user.
We plan to use the following applications as a vehicle for developing and evaluating security and privacy in mobile monitors:
The expected outcomes of the research will include: