Data Assurance in Medical Sensor Applications

Project Summary

We expect that wearable, portable, and even embeddable medical sensors will enable long-term continuous medical monitoring for many purposes, such as patients with chronic medical conditions (such as the recently announced blood-sugar sensors for diabetics), people seeking to change behavior (e.g., losing weight, or quitting smoking), or athletes wishing to monitor their condition and performance. The resulting data may be used directly by the person, or shared with others: with a physician for treatment, with an insurance company for coverage, or by a trainer or coach. Such systems have huge potential benefit to the quality of healthcare and quality of life for many people.

Since the sensor data may be gathered through a patient's mobile device (such as a mobile phone), a wireless network, and the Internet, there are many opportunities for the sensor data to be tampered or otherwise inaccurate. How can we assess confidence in sensor data? How can we present that level of confidence, in context, with the sensor data? This project will develop methods to assess confidence in medical sensor data.

• Principal Investigator: David Kotz, Department of Computer Science, Tanzeem Choudhury, Department of Computer Science
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• Funding: Intel University Research Council