• DocumentCode
    168674
  • Title

    Measuring activities and counting steps with the SmartSocks - An unobtrusive and accurate method

  • Author

    Jiang Lu ; Ting Zhang ; Fei Hu ; Yeqing Wu ; Ke Bao

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscaloosa, AL, USA
  • fYear
    2014
  • fDate
    10-13 Oct. 2014
  • Firstpage
    694
  • Lastpage
    698
  • Abstract
    Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Physical inactivity is the leading cause of many diseases. It has been estimated that as many as 250,000 deaths per year in the United States, approximately 12% of the total, are attributable to a lack of regular physical activity. Measuring physical activities and counting steps is an effective method to diagnose some diseases. It can also serve as an effective method to encourage people to increase their physical activity. Pedometers have been invented as a convenient way of counting steps. However most of them lack the functionality of differentiating activities. Pressure sensor pads can measure steps and gait, but as the pad has a limited size, it can not meet the need of anytime and anywhere usage. In this study, we made the Sensor Socks for measuring physical activities and counting steps. It is unobtrusive and convenient for everyday usage. Our experimental results show that the system has a high accuracy of the classification of physical activities and counting steps in a home or community environment.
  • Keywords
    biomedical measurement; diseases; gait analysis; medical computing; patient diagnosis; pressure sensors; SmartSocks; accurate method; counting steps; gait analysis; noncommunicable diseases diagnosis; pedometers; physical activity classification; physical inactivity measurement; pressure sensor; regular physical activity; sensor socks; unobtrusive method; Accuracy; Biomedical measurement; Computers; Diseases; Legged locomotion; Support vector machines; Vectors; Biomedicine; Data Mining; Machine Learning; Wearable Sensors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Global Humanitarian Technology Conference (GHTC), 2014 IEEE
  • Conference_Location
    San Jose, CA
  • Type

    conf

  • DOI
    10.1109/GHTC.2014.6970358
  • Filename
    6970358