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3D Tracking via Shoe Sensing

Sensors (Basel, Switzerland), 2016-10, Vol.16 (11), p.1809 [Peer Reviewed Journal]

2016. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2016 by the authors; licensee MDPI, Basel, Switzerland. 2016 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s16111809 ;PMID: 27801839

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  • Title:
    3D Tracking via Shoe Sensing
  • Author: Li, Fangmin ; Liu, Guo ; Liu, Jian ; Chen, Xiaochuang ; Ma, Xiaolin
  • Subjects: 3D positioning ; Accelerometers ; Accuracy ; Electronic devices ; Gait ; Global positioning systems ; GPS ; Indoor environments ; indoor localization ; Inertial sensing devices ; inertial sensor ; Location-based services ; Satellite navigation systems ; Sensors ; Shopping malls ; Tracking ; Walking ; walking state classification
  • Is Part Of: Sensors (Basel, Switzerland), 2016-10, Vol.16 (11), p.1809
  • Description: Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices' random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s16111809
    PMID: 27801839
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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