Improving human activity recognition using subspace clustering

Huiquan Zhang*, Osamu Yoshie

*この研究の対応する著者

研究成果: Conference contribution

10 被引用数 (Scopus)

抄録

Activity recognition attracted much interest in pervasive sensing due to extensive application in human daily life from health monitoring to security monitoring. It utilizes collection of data from low level sensor to learn about human behaviors and activities, so that services can be provided by function of detecting anomalies, remote interventions or prompts. The approach of human activity modeling and recognition still confronted with a challenge on issues of modeling human activity in human perspective. However, the traditional learning-based approaches are not sufficient to capture the characteristics of human activity because they still use traditional clustering method to process sensor data which consists of multidimensional information. This paper describes a subspace clustering-based approach to recognize human activity and detect exceptional activities. Different from many approaches, the proposed approach use subspace clustering based approach to model of human activity in order to improve accuracy of activity recognition. Finally, the proposed approach has been validated on data collected from RFID-based systems, which results demonstrate the effectiveness of the proposed improvents.

本文言語English
ホスト出版物のタイトルProceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
ページ1058-1063
ページ数6
DOI
出版ステータスPublished - 2012 12 31
イベント2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China
継続期間: 2012 7 152012 7 17

出版物シリーズ

名前Proceedings - International Conference on Machine Learning and Cybernetics
3
ISSN(印刷版)2160-133X
ISSN(電子版)2160-1348

Conference

Conference2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
国/地域China
CityXian, Shaanxi
Period12/7/1512/7/17

ASJC Scopus subject areas

  • 人工知能
  • 計算理論と計算数学
  • コンピュータ ネットワークおよび通信
  • 人間とコンピュータの相互作用

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