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Who Wears Me? Bioimpedance as a Passive Biometric
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Mobile and wearable systems for monitoring health are becoming common. If such an mHealth system knows the identity of its wearer, the system can properly label and store data collected by the system. Existing recognition schemes for such mobile applications and pervasive devices are not particularly usable – they require active engagement with the person (e.g., the input of passwords), or they are too easy to fool (e.g., they depend on the presence of a device that is easily stolen or lost).
We present a wearable sensor to passively recognize people. Our sensor uses the unique electrical properties of a person’s body to recognize their identity. More specifically, the sensor uses bioimpedance – a measure of how the body’s tissues oppose a tiny applied alternating current – and learns how a person’s body uniquely responds to alternating current of different frequencies. In this paper we demonstrate the feasibility of our system by showing its effectiveness at accurately recognizing people in a household 90% of the time.
Cory Cornelius, Jacob Sorber, Ronald Peterson, Joe Skinner, Ryan Halter, and David Kotz, Institute for Security, Technology, and Society, Dartmouth College
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Mobile and wearable systems for monitoring health are becoming common. If such an mHealth system knows the identity of its wearer, the system can properly label and store data collected by the system. Existing recognition schemes for such mobile applications and pervasive devices are not particularly usable – they require active engagement with the person (e.g., the input of passwords), or they are too easy to fool (e.g., they depend on the presence of a device that is easily stolen or lost).
We present a wearable sensor to passively recognize people. Our sensor uses the unique electrical properties of a person’s body to recognize their identity. More specifically, the sensor uses bioimpedance – a measure of how the body’s tissues oppose a tiny applied alternating current – and learns how a person’s body uniquely responds to alternating current of different frequencies. In this paper we demonstrate the feasibility of our system by showing its effectiveness at accurately recognizing people in a household 90% of the time.
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