Gait based biometric personal authentication by using MEMS inertial sensors

Gait based biometric personal authentication by using MEMS inertial sensors Walking is one of the major human activities, and walking pattern is unique for each individual. Thus, human gait can be applied in biometric personal authentication. The traditional method for gait recognition is based on one or multiple cameras. With the rapid development of Micro-Electro-Mechanical System (MEMS), small light inertial sensors have been used for human identification so far. In this study, a gait based personal authentication method is proposed using MEMS inertial sen- sors. They are fixed in the smart shoes, collecting motion signals and transmitting them to the server. Then, gait parameters such as step length, cadence, stance phase, swing phase and the pitch angular are calculated and used as features for personal identification. A probabilistic neural network is proposed as a classification mechanism to uniquely identify different users. Experiments are conducted to validate the proposed method. By using two cross-validation techniques, the overall mean classification rate for 22 persons is up to 85.3 and 85.7% respectively, which demonstrates the effectiveness of the method. Keywords Personal authentication · Inertial sensors · Gait parameters · Probabilistic neural network · Classification rate 1 Introduction et  al. 2012; Wu and Xue 2008) and personal authentica- tion (Derawi et  al. 2010; Hoang et  al. 2013; http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Ambient Intelligence and Humanized Computing Springer Journals

Gait based biometric personal authentication by using MEMS inertial sensors

Loading next page...
 
/lp/springer_journal/gait-based-biometric-personal-authentication-by-using-mems-inertial-ShejkY24DS
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Robotics and Automation; User Interfaces and Human Computer Interaction
ISSN
1868-5137
eISSN
1868-5145
D.O.I.
10.1007/s12652-018-0880-6
Publisher site
See Article on Publisher Site

Abstract

Walking is one of the major human activities, and walking pattern is unique for each individual. Thus, human gait can be applied in biometric personal authentication. The traditional method for gait recognition is based on one or multiple cameras. With the rapid development of Micro-Electro-Mechanical System (MEMS), small light inertial sensors have been used for human identification so far. In this study, a gait based personal authentication method is proposed using MEMS inertial sen- sors. They are fixed in the smart shoes, collecting motion signals and transmitting them to the server. Then, gait parameters such as step length, cadence, stance phase, swing phase and the pitch angular are calculated and used as features for personal identification. A probabilistic neural network is proposed as a classification mechanism to uniquely identify different users. Experiments are conducted to validate the proposed method. By using two cross-validation techniques, the overall mean classification rate for 22 persons is up to 85.3 and 85.7% respectively, which demonstrates the effectiveness of the method. Keywords Personal authentication · Inertial sensors · Gait parameters · Probabilistic neural network · Classification rate 1 Introduction et  al. 2012; Wu and Xue 2008) and personal authentica- tion (Derawi et  al. 2010; Hoang et  al. 2013;

Journal

Journal of Ambient Intelligence and Humanized ComputingSpringer Journals

Published: May 29, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off