Activity recognition based on SVM kernel fusion in smart home

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

*Corresponding author for this work

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

6 Citations (Scopus)


Smart home is regarded as an independent healthy living for elderly person and it demands active research in activity recognition. This paper proposes kernel fusion method, using Support Vector Machine (SVM) in order to improve the accuracy of performed activities. Although, SVM is a powerful statistical technique, but still suffer from the expected level of accuracy due to complex feature space. Designing a new kernel function is difficult task, while common available kernel functions are not adequate for the activity recognition domain to achieve high accuracy. We introduce a method, to train the different SVMs independently over the standard kernel functions and fuse the individual results on the decision level to increase the confidence of each activity class. The proposed approach has been evaluated on ten different kinds of activities from CASAS smart home (Tulum 2009) real world dataset. We compare our SVM kernel fusion approach with the standard kernel functions and get overall accuracy of 91.41 %.

Original languageEnglish
Title of host publicationComputer Science and Its Applications, CSA 2012
Number of pages8
Publication statusPublished - 2012
Externally publishedYes
Event4th FTRA International Conference on Computer Science and Its Applications, CSA 2012 - Jeju Island, Korea, Republic of
Duration: 22 Nov 201225 Nov 2012

Publication series

NameLecture Notes in Electrical Engineering
Volume203 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference4th FTRA International Conference on Computer Science and Its Applications, CSA 2012
Country/TerritoryKorea, Republic of
CityJeju Island

Bibliographical note

Funding Information:
This research was supported by the The Ministry of Knowledge Economy (MKE), Korea under the Information Technology Research Center (ITRC) support program supervised by the National Industry Promotion Agency (NIPA) [NIPA-2010-(C1090-1021-0003)].

Copyright 2012 Elsevier B.V., All rights reserved.


  • Activity recognition
  • Kernel fusion
  • Smart home
  • SVM

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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