• DocumentCode
    590517
  • Title

    Pedestrian activity detection in a multi-floor environment by a smart phone

  • Author

    Chi-Chung Lo ; Yi-Hsiu Chen ; Yu-Chee Tseng ; Shang-Ming Huang ; Yu-Neng Hung ; Chiu-Mei Tseng ; Yeh-Chin Ho

  • Author_Institution
    Dept. of Comput. Sci., Nat. Chiao-Tung Univ., Hsinchu, Taiwan
  • fYear
    2012
  • fDate
    28-31 Oct. 2012
  • Firstpage
    1
  • Lastpage
    2
  • Abstract
    Indoor localization has attracted considerable attention recently. One approach is to use inertial sensors mounted on pedestrians to characterize the users´ motions. However, few studies have focused on a multi floor environment where users´ activities may include walking, running, and going up/down stairs. This paper proposes a lightweight activity detection system using inertial sensors on a smart phone to detect the behaviors of a pedestrian in the multi-floor indoor environment. The system first identifies strides using the accelerations values. It then uses the displacement, duration, and acceleration to classify their types. Our experimental results show that the stride detection accuracy is about 99%. In addition, the types of strides, namely walking, running, going upstairs, and going downstairs, can be detected with the accuracy of 94%, 91%, 95%, and 92%, respectively.
  • Keywords
    indoor radio; mobile computing; object detection; pedestrians; sensors; smart phones; going downstairs; going upstairs; indoor localization; inertial sensors; lightweight activity detection system; multifloor indoor environment; pedestrian activity detection; running; smart phone; stride detection accuracy; walking; Acceleration; Accuracy; Intelligent sensors; Legged locomotion; Navigation; Smart phones;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Sensors, 2012 IEEE
  • Conference_Location
    Taipei
  • ISSN
    1930-0395
  • Print_ISBN
    978-1-4577-1766-6
  • Electronic_ISBN
    1930-0395
  • Type

    conf

  • DOI
    10.1109/ICSENS.2012.6411387
  • Filename
    6411387