Variance Based Pattern Detection for Inferring Activities of Daily Living


  • W. Ali Faculty of Telecom & Information Engineering, UET, Taxila
  • M. A. Azam Faculty of Telecom & Information Engineering, UET, Taxila
  • U. Naeem School of Architecture, Computing and Engineering, University of East London, London, UK
  • M. A. Ghazanfar Faculty of Telecom & Information Engineering, UET, Taxila
  • A. Khalid Computer Science Department, COMSATS, Wah Cantt
  • Y. Amin Faculty of Telecom & Information Engineering, UET, Taxila



Being able to recognize activities of daily living (ADLs) using non-intrusive devices is very much dependent on the ability to discover a range of patterns within captured datasets. Pattern recognition plays an important role as the presence of patterns in one instance and the absence in another presents an accurate means of classification. In this paper, we present a pattern recognition approach based on recognizing the emerging patterns for inferring ADLs. In order to validate this proposed approach, a series of activity datasets have been used for validation experiments.


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How to Cite

W. Ali, M. A. Azam, U. Naeem, M. A. Ghazanfar, A. Khalid, and Y. Amin, “Variance Based Pattern Detection for Inferring Activities of Daily Living”, The Nucleus, vol. 54, no. 2, pp. 127–134, Jun. 2017.




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