Applying hierarchical information with learning approach for activity recognition

  • Authors:

  • Hoai-Viet To;Hoai-Bac Le;Mitsuru Ikeda

  • Affiliations:

  • School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan;Faculty of Information Technology, University of Science, Vietnamese National University, Ho Chi Minh City, Vietnam;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

  • Venue:
  • IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper discusses the problem of applying ontology for activity recognition and proposes a hierarchical classification approach by using categorize information among activities with machine learning method. In activity recognition problem, machine learning approaches have the ability to adapt to various real environments but actual setting do not often obtain enough quality data to construct a good model for recognizing multiple activities. Our approach exploits the hierarchical structure of activities to overcome the problem uncertainty and incomplete data for multi-class classification in real home setting datasets. While slightly improves the overall recognition accuracy from 59% to 63%, hierarchical approach can recognize infrequent activities such as "Going out to work" and "Taking medication" with accuracies of 80% and 56% respectively. Those activities had recognition accuracies lower than random guess in previous learning method. The preliminary results support the idea to develop a methodology to utilize semantic information represented in ontologies for activity recognition problem.