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DALIAN UNIVERSITY OF TECHNOLOGY Login 中文
Wang Zhelong

Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates


Main positions:Professor, Head of Lab of Intelligent System
Other Post:自动化技术研究所所长
Gender:Male
Alma Mater:University of Durham
Degree:Doctoral Degree
School/Department:School of Control Science and Engineering
Discipline:Control Theory and Control Engineering. Pattern Recognition and Intelligence System. Detection Technology and Automation Device
Business Address:Lab of Intelligent System
http://lis.dlut.edu.cn/

Contact Information:0411-84709010 wangzl@dlut.edu.cn
E-Mail:wangzl@dlut.edu.cn
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Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System

Hits : Praise

Indexed by:Journal Papers

Date of Publication:2015-12-01

Journal:IEEE-ASME TRANSACTIONS ON MECHATRONICS

Included Journals:SCIE、EI、Scopus

Volume:20

Issue:6

Page Number:3170-3181

ISSN No.:1083-4435

Key Words:Inertial measurement unit (IMU); inertial navigation system (INS); pedestrian navigation system (PNS); stancephase detection; zero velocity updates (ZUPT)

Abstract:Zero velocity updates (ZUPT) is an effective way for the foot-mounted inertial pedestrian navigation systems. For the ZUPT technique to work properly, it is necessary to correctly detect the stance phase of each gait cycle. An adaptive stance-phase detection method is proposed based solely on an inertial sensor, which deals with the measurement fluctuations in swing and stance phases differently, and applies a clustering algorithm to partition the potential gait phases into true and false clusters, thereby yielding a time threshold to eliminate the false gait phases. The roles of the detection parameters and the relationship between them are analyzed to offer some suggestions for parameter tuning. Detection performance is evaluated with multisubject experimental data collected at varying walking speeds. The evaluation results show that the proposed detection method performs well in the presence of measurement fluctuations, which can make the detection of stance phases more robust and the choice of detection parameters more flexible.