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The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition

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Abstract

Two serious problems affecting the implementation of human activity recognition algorithms have been acknowledged. The first one corresponds to non-informative sequence features. The second is the class imbalance in the training data due to the fact that people do not spend the same amount of time on the different activities. To address these issues, we propose a new scheme based on a combination of principal component analysis, linear discriminant analysis (LDA) and the modified weighted support vector machines. First we added the most significant principal components to the set of features extracted using LDA. This work shows that a suitable sequence feature set combined with the modified WSVM based on our criterion classifier achieves good improvement and efficiency over the traditional used methods.

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Authors and Affiliations

  1. Laboratoire d’Ingénierie des Systèmes Intelligents et Communicants, LISIC Laboratory, Electronics and Computer Sciences Department, University of Science and Technology Houari Boumediene (USTHB), 32, El Alia, 16111, Bab Ezzouar, Algiers, Algeria

    Bilal M’hamed Abidine, Lamya Fergani & Belkacem Fergani

  2. Electrical and Computer Engineering Department, University of Birmingham, Birmingham, UK

    Mourad Oussalah

  3. Centre for Ubiquitous Computing, University of Oulu, 90014, Oulu, Finland

    Mourad Oussalah

Authors
  1. Bilal M’hamed Abidine
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  2. Lamya Fergani
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  3. Belkacem Fergani
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  4. Mourad Oussalah
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Correspondence to Belkacem Fergani.

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Abidine, B.M., Fergani, L., Fergani, B. et al. The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition. Pattern Anal Applic 21, 119–138 (2018). https://doi.org/10.1007/s10044-016-0570-y

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