Activity recognition: An evolutionary ensembles approach

Muhammad Fahim*, Iram Fatima, Sungyoung Lee, Young Koo Lee

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

Activity recognition is an emerging field that demands active research in ubiquitous computing for analyzing complex scenarios such as concurrent situation assessment and domination of major over the minor activities. In this paper, an evolutionary ensembles approach using Genetic Algorithm (GA) as a homogeneous learner has been proposed. This approach values both minor and major activities by processing each of them independently. It consists of two phases. The first phase is preprocessing of sensory data and extraction of feature vectors. Evolutionary ensembles are designed in second phase to learn different daily life activities. Finally, multiple ensembles output is pooled on central node as a complete rule profile for all performed activities. The proposed approach was evaluated on six different types of activities from Intelligent System Laboratory (ISL) dataset. It shows a higher accuracy as compared to single learner GA.

Original languageEnglish
Title of host publicationSAGAware'11 - Proceedings of the 2011 International Workshop on Situation Activity and Goal Awareness
Pages45-49
Number of pages5
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 International Workshop on Situation Activity and Goal Awareness, SAGAware'11, Co-located with UbiComp 2011 - Beijing, China
Duration: 18 Sept 201118 Sept 2011

Publication series

NameSAGAware'11 - Proceedings of the 2011 International Workshop on Situation Activity and Goal Awareness

Conference

Conference2011 International Workshop on Situation Activity and Goal Awareness, SAGAware'11, Co-located with UbiComp 2011
Country/TerritoryChina
CityBeijing
Period18/09/201118/09/2011

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

Keywords

  • activity recognition
  • ensemble learning
  • genetic algorithm

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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