Human activities recognition based on neuro-fuzzy finite state machine

Mohmed, G., Lotfi, A. ORCID: 0000-0002-5139-6565 and Pourabdollah, A. ORCID: 0000-0001-7737-1393, 2018. Human activities recognition based on neuro-fuzzy finite state machine. Technologies, 6 (4): 110. ISSN 2227-7080

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Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

Item Type: Journal article
Publication Title: Technologies
Creators: Mohmed, G., Lotfi, A. and Pourabdollah, A.
Publisher: MDPI
Date: 2018
Volume: 6
Number: 4
ISSN: 2227-7080
technologies6040110Publisher Item Identifier
Rights: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 05 Dec 2018 12:18
Last Modified: 05 Dec 2018 12:18

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