Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

Mohmed, Gadelhag; Lotfi, Ahmad; Pourabdollah, Amir

doi:10.3390/technologies6040110

Cited by 7 publications

(7 citation statements)

References 37 publications

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“…This hybrid classifier results in an intelligent inference system which is capable of both reasoning and selflearning [84]. Many action recognition systems based on NFC have been proposed in recent years [85]. This is a six-layer architecture, as displayed in Figure 9.…”

Section: Neuro-fuzzy Classifier (Nfc)mentioning

confidence: 99%

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

et al. 2021

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Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra depth of information which enables the three-dimensional (3D) tracking of each and every movement performed by humans. Human actions are tracked according to four features, namely, (1) geodesic distance; (2) 3D Cartesian-plane features; (3) joints Motion Capture (MOCAP) features and (4) way-points trajectory generation. In order to represent these features in an optimized form, Particle Swarm Optimization (PSO) is applied. After optimization, a neuro-fuzzy classifier is used for classification and recognition. Extensive experimentation is performed on three challenging datasets: A Nanyang Technological University (NTU) RGB+D dataset; a UoL (University of Lincoln) 3D social activity dataset and a Collective Activity Dataset (CAD). Evaluation experiments on the proposed system proved that a fusion of vision sensors along with our unique features is an efficient approach towards developing a robust HAR system, having achieved a mean accuracy of 93.5% with the NTU RGB+D dataset, 92.2% with the UoL dataset and 89.6% with the Collective Activity dataset. The developed system can play a significant role in many computer vision-based applications, such as intelligent homes, offices and hospitals, and surveillance systems.

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Section: Neuro-fuzzy Classifier (Nfc)mentioning

confidence: 99%

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

et al. 2021

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“…Fuzzy Finite State Machine (FFSM) is an extended version of the classical FSM. The FSM can be presented as a model made of two or more states; each state represents one event from a sequence of events in a dynamic process (Mohmed et al 2018b). Only one single state of this model can be active at a time.…”

Section: Fuzzy Finite State Machinementioning

confidence: 99%

“…For a rule-base consisting of rules, the next value of the state vector S(t + 1) is the weighted average utilising the firing degree of each rule (Mohmed et al 2018b), defined as:…”

Section: Fuzzy Finite State Machinementioning

confidence: 99%

“…Readers are referred to (Langensiepen et al 2014;Alvarez-Alvarez et al 2012;Ambres and Trivino 2012;Mohmed et al 2018b) for more details about FFSM. More details (4)…”

Section: Fuzzy Finite State Machinementioning

confidence: 99%

“…One of the promising techniques to model dynamic processes when data changes over time is the Finite State Machine (FSM) (Mohmed et al 2018b;Alvarez-Alvarez et al 2012). The FSM contains several states representing different actions and the mechanism of transitions between them.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced fuzzy finite state machine for human activity modelling and recognition

Mohmed

Lotfi

Pourabdollah

2020

J Ambient Intell Human Comput

Self Cite

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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 $$95.7\%$$ 95.7 % and $$97.6\%$$ 97.6 % for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were $$94.2\%$$ 94.2 % and $$99.3\%$$ 99.3 % , respectively.

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Fuzzy Feature Representation with Bidirectional Long Short-Term Memory for Human Activity Modelling and Recognition

Mohmed

Adama

Lotfi

2019

Advances in Intelligent Systems and Computing

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Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

Cited by 7 publications

References 37 publications

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

Enhanced fuzzy finite state machine for human activity modelling and recognition

Fuzzy Feature Representation with Bidirectional Long Short-Term Memory for Human Activity Modelling and Recognition

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