Activity recognition based on wearable sensors using selection/fusion hybrid ensemble

Jun Ki Min, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Citations (Scopus)

Abstract

Activity recognition with mobile sensors is a challenging task due to the inherent noisy nature of the input data and resource limitations of the target platform. This paper presents a novel method of hybridizing classifier selection and classifier fusion in order to address these difficulties. It efficiently decreases the computational cost by activating appropriate classifiers according to the characteristics of the given input, and resolves the pattern variations by combining the chosen classifiers with localized templates. The proposed method is integrated with a wearable system that includes five motion sensors (accelerometers and gyroscopes), a set of bio-signal sensors, and data-gloves. The experiments on two different levels of activities, such as 11 primitive motions and eight composite behaviors, demonstrated that the proposed method is useful to the wearable systems.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Pages1319-1324
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States
Duration: 2011 Oct 92011 Oct 12

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Other

Other2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
Country/TerritoryUnited States
CityAnchorage, AK
Period11/10/911/10/12

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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