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Zensei: Embedded, Multi-electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition
MIT, Media Lab, Cambridge, MA 02139 USA.;Univ Tokyo, Tokyo, Japan..
MIT, Media Lab, Cambridge, MA 02139 USA..
KTH. MIT, Media Lab, Cambridge, MA 02139 USA.;Google Inc, Mountain View, CA USA..
Takram London, London, England..
Show others and affiliations
2017 (English)In: PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), ASSOC COMPUTING MACHINERY , 2017, p. 3972-3985Conference paper, Published paper (Refereed)
Abstract [en]

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 classification accuracy and low false acceptance rate.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY , 2017. p. 3972-3985
Keywords [en]
Implicit sensing, User recognition, Ubiquitous computing, Electrical sensing, Embedded devices, Bio-sensing
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-225814DOI: 10.1145/3025453.3025536ISI: 000426970503077OAI: oai:DiVA.org:kth-225814DiVA, id: diva2:1196035
Conference
PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17)
Note

QC 20180409

Available from: 2018-04-09 Created: 2018-04-09 Last updated: 2018-04-09Bibliographically approved

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