By Topic

Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Khan, A.M. ; Dept. of Comput. Eng., Kyung Hee Univ., Suwon, South Korea ; Lee, Y.-K. ; Lee, S.Y. ; Kim, T.-S.

Nowadays many people use smartphones with built-in accelerometers which makes these smartphones capable of recognizing daily activities. However, mobile phones are carried along freely instead of a firm attachment to a body part. Since the output of any body-worn triaxial accelerometer varies for the same physical activity at different positions on a subject's body, the acceleration data thus could vary significantly for the same activity which could result in high within-class variance. Therefore, realization of activity-aware smartphones requires a recognition method that could function independent of phone's position along subjects' bodies. In this study, we present a method to address this problem. The proposed method is validated using five daily physical activities. Activity data is collected from five body positions using a smartphone with a built-in triaxial accelerometer. Features including autoregressive coefficients and signal magnitude area are calculated. Kernel Discriminant Analysis is then employed to extract the significant non-linear discriminating features which maximize the between-class variance and minimize the within-class variance. Final classification is performed by means of artificial neural nets. The average accuracy of about 96% illustrates the effectiveness of the proposed method.

Published in:

Future Information Technology (FutureTech), 2010 5th International Conference on

Date of Conference:

21-23 May 2010