NDLI: A new inertial sensor-based gait recognition method via deterministic learning

Content Provider	IEEE Xplore Digital Library
Author	Zeng Wei Wang Qinghui Deng Muqing Liu Yiqi
Copyright Year	2015
Description	Author affiliation: Sch. of Mech. & Electr. Eng., Longyan Univ., Longyan, China (Zeng Wei; Wang Qinghui) \|\| Sch. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China (Deng Muqing; Liu Yiqi)
Abstract	This paper presents a new gait recognition method based on acceleration and angular velocity data captured by inertial sensors via deterministic learning. These gait features describe the motion trajectories of human gait and contain rich information for persons identification. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, the gait dynamics underlying different individuals' gaits are represented by the acceleration and angular velocity features, and are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated. The average $L_{1}$ norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, comprehensive experiments are carried out on the OU-ISIR biometric gait database: inertial sensor dataset, which includes at most 744 subjects (389 males and 355 females) and is now the world's largest inertial sensor-based gait database, to demonstrate the recognition performance of the proposed algorithm.
Starting Page	3908
Ending Page	3913
File Size	370055
Page Count	6
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260243
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Training Legged locomotion Gait Recognition Acceleration and Angular Velocity Features Databases Gait Dynamics Radial basis function networks Feature extraction Deterministic Learning Sensors Inertial Sensor Gait recognition
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

View-invariant gait recognition via deterministic learning

Alzheimer's disease classification based on gait information

Biometric gait recognition based on wireless acceleration sensor using k-nearest neighbor classification

Cepstral based features for gait recognition

Radial Basis Function (RBF) networks for improved gait analysis

Activity-based human identification

A new method of gait recognition independent of view angle

Online sequential extreme learning machine algorithm based human activity recognition using inertial data

A novel approach of gait recognition through fusion with footstep information

A new inertial sensor-based gait recognition method via deterministic learning

Similar Documents

View-invariant gait recognition via deterministic learning

Alzheimer's disease classification based on gait information

Biometric gait recognition based on wireless acceleration sensor using k-nearest neighbor classification

Cepstral based features for gait recognition

Radial Basis Function (RBF) networks for improved gait analysis

Activity-based human identification

A new method of gait recognition independent of view angle

Online sequential extreme learning machine algorithm based human activity recognition using inertial data

A novel approach of gait recognition through fusion with footstep information

A new inertial sensor-based gait recognition method via deterministic learning