ICRA 2011 Paper Abstract


Paper TuP104.4

Chen, Xi (Michigan Technological University), Hu, Sheng (Michigan Technological University), Shao, Zhenzhou (MTU), Tan, Jindong (Michigan Technological University)

Pedestrian Positioning with Physical Activity Classification for Indoors

Scheduled for presentation during the Regular Sessions "Human Detection and Tracking II" (TuP104), Tuesday, May 10, 2011, 14:25−14:40, Room 3E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Human detection & tracking, Automation in Life Sciences: Biotechnology, Pharmaceutical and Health Care, Sensor Networks


This paper presents a wearable Inertial Measurement Unit pedestrian positioning system for indoors. Hidden Markov Model (HMM) is introduced to pre-process the sensor data and classify common activities. HMM also complements local minimum angular rate value for capturing the onset/end of each step. ZUPT algorithm are implemented to correct the walking velocity at step stance phase when errors existed. A novel acceleration-based approach combined with gyroscope data is developed to achieve a better heading estimation. Proposed method is able to reduce drift errors from gyroscopes and avoid electromagnetic perturbance to magnetometers when estimate subject's position. Experiment results show the positioning system achieves approximately 99% accuracy.



Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2020 PaperCept, Inc.
Page generated 2020-04-02  12:42:02 PST  Terms of use