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Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization

  • Belkacem ChikhaouiEmail author
  • Bing Ye
  • Alex Mihailidis
Original Research

Abstract

This paper presents a novel approach for aggressive and agitated behavior recognition using accelerometer data. Our approach applies first a noise reduction technique using the moving average filter method. Then, multiple features such as mean, variance, entropy, correlation and covariance are extracted from the filtered acceleration data using a sliding window. Non-negative matrix factorization is then used in order to project the data into a new reduced space that captures the significant structure of the data. The recognition is performed using the rotation forest ensemble method. The proposed approach is validated using extensive experiments on a real dataset collected at Toronto Rehabilitation Institute. We empirically demonstrate that our proposed approach accurately discriminates between behaviors and performs better than several state-of-the-art approaches.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.TELUQ UniversityMontrealCanada
  2. 2.IATSL Laboratory, Toronto Rehab InstituteUniversity of TorontoTorontoCanada

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