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Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones

Authors:

Han ZouAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 374 - 385

https://doi.org/10.1145/2971648.2971742

Published: 12 September 2016 Publication History

Abstract

Pedestrian dead reckoning (PDR) is a promising complementary technique to balance the requirements on both accuracy and costs in outdoor and indoor positioning systems. In this paper, we propose a unified framework to comprehensively tackle the three sub problems involved in PDR, including step detection and counting, heading estimation and step length estimation, based on sequentially rotating the device (reference) frame to the Earth (reference) frame through sensor fusion. To be specific, a robust step detection and counting algorithm is devised according to vertical angular velocities and turns out to be tolerant of various smartphone placements; then, a zero velocity update (ZUPT) based algorithm is leveraged to calibrate the measurements in the Earth frame; on these grounds, the heading and step length are further estimated by exploiting the cyclic features of walking. A thorough and extensive experimental analysis is conducted and confirms the effectiveness and advantages of the proposed PDR framework as well as the corresponding algorithms.

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Cited By

Tong YWang JZhao ZCui JWu B(2024)Noisy Labels Make Sense: Data-Driven Smartphone Inertial Tracking without Tedious Annotations2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)10.1109/WoWMoM60985.2024.00061(339-348)Online publication date: 4-Jun-2024
https://doi.org/10.1109/WoWMoM60985.2024.00061
Chen CPan X(2024)Deep Learning for Inertial Positioning: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338116125:9(10506-10523)Online publication date: Sep-2024
https://doi.org/10.1109/TITS.2024.3381161
Xu ZHuang BJia BMao G(2024)Enhancing WiFi Fingerprinting Localization Through a Co-Teaching Approach Using Crowdsourced Sequential RSS and IMU DataIEEE Internet of Things Journal10.1109/JIOT.2023.329752111:2(3550-3562)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3297521
Show More Cited By

Index Terms

Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2016

1288 pages

ISBN:9781450344616

DOI:10.1145/2971648

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 September 2016

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Grassland Elite Project of Inner Mongolia Autonomous Region
National Natural Science Foundation of China
Natural Science Foundation of Inner Mongolia

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UbiComp '16

Sponsor:

UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

Acceptance Rates

UbiComp '16 Paper Acceptance Rate 101 of 389 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Tong YWang JZhao ZCui JWu B(2024)Noisy Labels Make Sense: Data-Driven Smartphone Inertial Tracking without Tedious Annotations2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)10.1109/WoWMoM60985.2024.00061(339-348)Online publication date: 4-Jun-2024
https://doi.org/10.1109/WoWMoM60985.2024.00061
Chen CPan X(2024)Deep Learning for Inertial Positioning: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338116125:9(10506-10523)Online publication date: Sep-2024
https://doi.org/10.1109/TITS.2024.3381161
Xu ZHuang BJia BMao G(2024)Enhancing WiFi Fingerprinting Localization Through a Co-Teaching Approach Using Crowdsourced Sequential RSS and IMU DataIEEE Internet of Things Journal10.1109/JIOT.2023.329752111:2(3550-3562)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3297521
Park JHong Lee JGook Park C(2024)Pedestrian Stride Length Estimation Based on Bidirectional LSTM and CNN ArchitectureIEEE Access10.1109/ACCESS.2024.345404912(124718-124728)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3454049
Huang GLiu JYang SGong XZhao Y(2024)Context-assisted personalized pedestrian dead reckoning localization with a smartphoneGeo-spatial Information Science10.1080/10095020.2024.2338225(1-17)Online publication date: 17-Apr-2024
https://doi.org/10.1080/10095020.2024.2338225
Yang YHuang BXu ZYang R(2023)A Fuzzy Logic-Based Energy-Adaptive Localization Scheme by Fusing WiFi and PDRWireless Communications & Mobile Computing10.1155/2023/90524772023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9052477
Tan JWu HChow KChan S(2023)Implicit Multimodal Crowdsourcing for Joint RF and Geomagnetic FingerprintingIEEE Transactions on Mobile Computing10.1109/TMC.2021.308826822:2(935-950)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TMC.2021.3088268
Chen JYang KZheng XDong SLiu LMa H(2023)WiMix: A Lightweight Multimodal Human Activity Recognition System based on WiFi and Vision2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS58611.2023.00057(406-414)Online publication date: 25-Sep-2023
https://doi.org/10.1109/MASS58611.2023.00057
Shu MChen GZhang Z(2022)EL-SLE: Efficient Learning Based Stride-Length Estimation Using a SmartphoneSensors10.3390/s2218686422:18(6864)Online publication date: 10-Sep-2022
https://doi.org/10.3390/s22186864
Luu LPillai ALea HBuendia RKhan FDennis G(2022)Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer DataSensors10.3390/s2211398922:11(3989)Online publication date: 24-May-2022
https://doi.org/10.3390/s22113989
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