Analysis of symbolic models of biometric data and their use for action and user identification

Document Type

Conference Proceeding

Source of Publication

2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2018

Publication Date

7-5-2018

Abstract

© 2018 IEEE. Smart devices are becoming an extension of our- selves that contain sensitive information and are often targeted for theft. The development of an intelligent and reliable means of user identification and authentication is critical. Not only can the development of user models performing tasks be used for user and task identification, but systems can also notify individuals if there is a potential health concern. The construction of an idealized model of human locomotion may give medical care providers a better understanding of individual differences and guide therapy and treatment. Data was gathered from a smartwatch worn by six subjects performing five different tasks and Genetic Programming was used to perform symbolic regression - a model free, nonlinear type of regression analysis. Symbolic regression was applied to smartwatch data and a collection of nonlinear closed form symbolic mathematical models were generated. Not only did these models fit the data well, but they provided insight into the underlying system. With only 5 seconds of unseen data, the models could classify which subjects were performing which task with 83.9% accuracy when chance was only 3.33%.

ISBN

9781538613993

Publisher

Institute of Electrical and Electronics Engineers Inc.

First Page

1

Last Page

8

Disciplines

Computer Sciences

Keywords

Biomechanics, Gait recognition, Genetic Pro-gramming, Identifi-cation, Kinetics, Smartwatch, Symbolic regression

Scopus ID

85051051477

Indexed in Scopus

yes

Open Access

no

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