0

Full Content is available to subscribers

Subscribe/Learn More  >

Pattern Classification Based on Sparse Representation

[+] Author Affiliations
Shaopeng Liu, Robert X. Gao

University of Connecticut, Storrs, CT

Dinesh John, John Staudenmayer, Patty S. Freedson

University of Massachusetts, Amherst, MA

Paper No. DSCC2012-MOVIC2012-8678, pp. 737-742; 6 pages
doi:10.1115/DSCC2012-MOVIC2012-8678
From:
  • ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference
  • Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems
  • Fort Lauderdale, Florida, USA, October 17–19, 2012
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4530-1
  • Copyright © 2012 by ASME

abstract

This paper presents a pattern classification method based on sparse representation. This new method bypasses the need for feature extraction and selection that are typically presented in the conventional classification methods, and performs classification using raw sensor signals directly. The performance of this new method is evaluated in the context of human physical activity assessment. Experimental results obtained from 105 human subjects demonstrate higher discriminative power than using the conventional k-nearest neighbor algorithm, verifying the effectiveness of the sparse representation method.

Copyright © 2012 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In