Enhanced fuzzy finite state machine for human activity modelling and recognition

Mohmed, G., Lotfi, A. ORCID: 0000-0002-5139-6565 and Pourabdollah, A. ORCID: 0000-0001-7737-1393, 2020. Enhanced fuzzy finite state machine for human activity modelling and recognition. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

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Abstract

A challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing the human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system’s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved 95.7% and 97.6% for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were 94.2% and 99.3%, respectively.

Item Type: Journal article
Publication Title: Journal of Ambient Intelligence and Humanized Computing
Creators: Mohmed, G., Lotfi, A. and Pourabdollah, A.
Publisher: Springer
Date: 30 April 2020
ISSN: 1868-5137
Identifiers:
NumberType
10.1007/s12652-020-01917-zDOI
1322094Other
Rights: © The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 04 May 2020 10:29
Last Modified: 22 Jun 2020 09:08
URI: http://irep.ntu.ac.uk/id/eprint/39780

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