By Topic

Manifold Learning and Recognition of Human Activity Using Body-Area Sensors

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

2 Author(s)
Mi Zhang ; Ming Hsieh Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA ; Sawchuk, A.A.

Manifold learning is an important technique for effective nonlinear dimensionality reduction in machine learning. In this paper, we present a manifold-based framework for human activity recognition using wearable motion sensors. In our framework, we use locally linear embedding (LLE) to capture the intrinsic structure and build nonlinear manifolds for each activity. A nearest-neighbor interpolation technique is then applied to learn the mapping function from the input space to the manifold space. Finally, activity recognition is performed by comparing trajectories of different activity manifolds in the manifold space. Experimental results validate the effectiveness of our framework and demonstrate that manifold learning is promising for the task of human activity recognition using wearable motion sensors.

Published in:

Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on  (Volume:2 )

Date of Conference:

18-21 Dec. 2011