Gaussian Process Person Identifier Based on Simple Floor Sensors

Authors:
Jaakko Suutala;Kaori Fujinami;Juha Röning
Affiliations:
Intelligent Systems Group, Infotech Oulu, University of Oulu, Finland;Department of Computer, Information and Communication Sciences, Tokyo University of Agriculture and Technology, Japan;Intelligent Systems Group, Infotech Oulu, University of Oulu, Finland
Venue:
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
Year:
2008

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Abstract

This paper describes methods and sensor technology used to identify persons from their walking characteristics. We use an array of simple binary switch floor sensors to detect footsteps. Feature analysis and recognition are performed with a fully discriminative Bayesian approach using a Gaussian Process (GP) classifier. We show the usefulness of our probabilistic approach on a large data set consisting of walking sequences of nine different subjects. In addition, we extract novel features and analyse practical issues such as the use of different shoes and walking speeds, which are usually missed in this kind of experiment. Using simple binary sensors and the large nine-person data set, we were able to achieve promising identification results: a 64% total recognition rate for single footstep profiles and an 84% total success rate using longer walking sequences (including 5 - 7-footstep profiles). Finally, we present a context-aware prototype application. It uses person identification and footstep location information to provide reminders to a user.