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Clustering-Based Fuzzy Finite State Machine for Human Activity Recognition

  • Gadelhag Mohmed
  • Ahmad Lotfi
  • Caroline Langensiepen
  • Amir Pourabdollah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)

Abstract

In this paper, a clustering-based fuzzy finite state machine approach for human activity modelling and recognition is proposed. It Incorporates the Fuzzy C-means (FCMs) clustering algorithm with a Fuzzy Finite State Machine (FuFSM) in order to generate the state transitions more effectively. This unsupervised approach will overcome the deficiency in identifying the knowledge-base required for FuFSM. To validate the proposed approach, experimental results are presented. The activities of two office workers are modelled/recognised using the proposed method. The approach taken for this research is based on ambient Intelligent sensory data rather than data coming from wearable sensors.

Keywords

Activity of daily working ADW Human behaviour Fuzzy finite state machine Fuzzy C-means clustering 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gadelhag Mohmed
    • 1
  • Ahmad Lotfi
    • 1
  • Caroline Langensiepen
    • 1
  • Amir Pourabdollah
    • 1
  1. 1.School of Science and TechnologyNottingham Trent UniversityNottinghamUK

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