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Unsupervised Learning Fuzzy Finite State Machine for Human Activities Recognition

Published: 26 June 2018 Publication History

Abstract

Human Activities Recognition (HAR) based on low-level sensory data has become an active research topic and attracting attention in many application domains. Many approaches are employed to process and analyse the collected sensory data for modelling and representing Activity of Daily Working (ADW) and/or Activity of Daily Living (ADL). In this paper, a novel method based on Fuzzy Finite State Machine (FuFSM) is presented to model the daily activities. The proposed method is using FuFSM integrated with Fuzzy C-Means (FCMs) clustering algorithm to overcome the challenges of defining simultaneous activities. Therefore, different states of activities could be represented with a degree of fuzziness. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a set of data that has been collected from an office environment.

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Cited By

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  • (2022)Human Activity Recognition Data Analysis: History, Evolutions, and New TrendsSensors10.3390/s2209340122:9(3401)Online publication date: 29-Apr-2022
  • (2020)Modeling, learning, and simulating human activities of daily living with behavior treesKnowledge and Information Systems10.1007/s10115-020-01476-xOnline publication date: 1-Jun-2020
  • (2018)Human Activities Recognition Based on Neuro-Fuzzy Finite State MachineTechnologies10.3390/technologies60401106:4(110)Online publication date: 26-Nov-2018
  • Show More Cited By

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cover image ACM Other conferences
PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
June 2018
591 pages
ISBN:9781450363907
DOI:10.1145/3197768
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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  • NSF: National Science Foundation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2018

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Author Tags

  1. Activities of daily working
  2. fuzzy C means
  3. fuzzy finite state machine
  4. human behaviour
  5. unsupervised learning technique

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Cited By

View all
  • (2022)Human Activity Recognition Data Analysis: History, Evolutions, and New TrendsSensors10.3390/s2209340122:9(3401)Online publication date: 29-Apr-2022
  • (2020)Modeling, learning, and simulating human activities of daily living with behavior treesKnowledge and Information Systems10.1007/s10115-020-01476-xOnline publication date: 1-Jun-2020
  • (2018)Human Activities Recognition Based on Neuro-Fuzzy Finite State MachineTechnologies10.3390/technologies60401106:4(110)Online publication date: 26-Nov-2018
  • (2018)Clustering-Based Fuzzy Finite State Machine for Human Activity RecognitionAdvances in Computational Intelligence Systems10.1007/978-3-319-97982-3_22(264-275)Online publication date: 11-Aug-2018

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