Zensei: Embedded, Multi-electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition
Abstract
Interactions and connectivity is increasingly expanding to
shared objects and environments, such as furniture, vehicles,
lighting, and entertainment systems. For transparent personalization
in such contexts, we see an opportunity for embedded
recognition, to complement traditional, explicit authentication.
We introduce Zensei, an implicit sensing system that leverages
bio-sensing, signal processing and machine learning to classify
uninstrumented users by their body’s electrical properties.
Zensei could allow many objects to recognize users. E.g.,
phones that unlock when held, cars that automatically adjust
mirrors and seats, or power tools that restore user settings.
We introduce wide-spectrum bioimpedance hardware that
measures both amplitude and phase. It extends previous approaches
through multi-electrode sensing and high-speed wireless
data collection for embedded devices. We implement
the sensing in devices and furniture, where unique electrode
configurations generate characteristic profiles based on user’s
unique electrical properties. Finally, we discuss results from
a comprehensive, longitudinal 22-day data collection experiment
with 46 subjects. Our analysis shows promising classifi-
cation accuracy and low false acceptance rate.
shared objects and environments, such as furniture, vehicles,
lighting, and entertainment systems. For transparent personalization
in such contexts, we see an opportunity for embedded
recognition, to complement traditional, explicit authentication.
We introduce Zensei, an implicit sensing system that leverages
bio-sensing, signal processing and machine learning to classify
uninstrumented users by their body’s electrical properties.
Zensei could allow many objects to recognize users. E.g.,
phones that unlock when held, cars that automatically adjust
mirrors and seats, or power tools that restore user settings.
We introduce wide-spectrum bioimpedance hardware that
measures both amplitude and phase. It extends previous approaches
through multi-electrode sensing and high-speed wireless
data collection for embedded devices. We implement
the sensing in devices and furniture, where unique electrode
configurations generate characteristic profiles based on user’s
unique electrical properties. Finally, we discuss results from
a comprehensive, longitudinal 22-day data collection experiment
with 46 subjects. Our analysis shows promising classifi-
cation accuracy and low false acceptance rate.
Research Areas
