Title

Recognizing Household Activities From Human Motion Data Using Active Learning And Feature Selection

Keywords

Active learning; Activity recognition; Conditional random fields; Feature selection; Support vector machines

Abstract

The ability to accurately recognize human household activities is an important stepping stone toward creating home living assistance systems in the future. Classifying these activities can be difficult due to noisy sensor data, lack of labeled training samples for rare actions and large individual differences in activity execution. In this article, we present two techniques for improving the supervised classification of human activities from motion data: 1) an active learning framework to improve sample efficiency and 2) intelligent feature selection to reduce training time. We demonstrate our techniques using the CMU Multimodal Activity database. © 2010-IOS Press and the authors. All rights reserved.

Publication Date

6-25-2010

Publication Title

Technology and Disability

Volume

22

Issue

1-2

Number of Pages

17-26

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.3233/TAD-2010-0284

Socpus ID

77953763789 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953763789

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