• DocumentCode
    3580674
  • Title

    Activity Estimation Using Regression Technique

  • Author

    Bhuvaneswari, P.T.V. ; Gayathri, S. ; Priyadharshini, A. Saraswathi

  • Author_Institution
    Dept. of Electron. Eng., Anna Univ., Chennai, India
  • fYear
    2014
  • Firstpage
    1177
  • Lastpage
    1183
  • Abstract
    Estimation of human activities using regression techniques has been performed in this paper. The activities considered for investigation are sitting, standing and walking. As the number of independent variable considered in the proposed work is more than two, Multiple Variate Regression (MVR) technique is applied to estimate the activities. From the performance analysis, it is found that this technique results in an overall accuracy of 99.8% of the activity sitting, 99.85% of the activity standing and 99.95% for the activity walking. However, the process involved in estimation is found to be time consuming. As the process is carried out in real time, in order to reduce the time consumption, the study is extended to Multiple Linear Regression (MLR) technique. From the analysis, it is found that MLR technique yields better result when compared to the MVR technique in terms of time consumption without compromising accuracy.
  • Keywords
    accelerometers; gesture recognition; regression analysis; signal classification; ubiquitous computing; MLR technique; MVR technique; activity classification; human activity estimation; multiple linear regression technique; multiple variate regression technique; sitting; standing; tri-axial accelerometer data; ubiquitous computing; walking; Accuracy; Decision support systems; Equations; Estimation; Linear regression; Mathematical model; Vectors; Accuracy and Time consumption; Activity Estimation; MLR; MVR; Regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Communication Networks (CICN), 2014 International Conference on
  • Print_ISBN
    978-1-4799-6928-9
  • Type

    conf

  • DOI
    10.1109/CICN.2014.246
  • Filename
    7065666