ORCID Profile
0000-0002-5139-6565
Current Organisation
Nottingham Trent University
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Publisher: Springer Science and Business Media LLC
Date: 26-11-2015
Publisher: MDPI AG
Date: 09-09-2016
DOI: 10.3390/S16091464
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2020
Publisher: ACM
Date: 21-06-2017
Publisher: ACM
Date: 21-06-2017
Publisher: Springer International Publishing
Date: 11-08-2018
Publisher: ACM
Date: 21-06-2017
Publisher: ACM
Date: 29-06-2016
Publisher: Springer International Publishing
Date: 11-08-2018
Publisher: ACM
Date: 26-06-2018
Publisher: ACM
Date: 29-06-2016
Publisher: Elsevier BV
Date: 02-2021
Publisher: ACM
Date: 26-06-2018
Publisher: ACM
Date: 29-06-2016
Publisher: IEEE
Date: 07-2010
Publisher: Springer International Publishing
Date: 11-08-2019
Publisher: MDPI AG
Date: 10-03-2018
Publisher: Emerald
Date: 12-2010
Publisher: Informa UK Limited
Date: 27-07-2010
Publisher: Springer Science and Business Media LLC
Date: 30-10-2019
Publisher: IEEE
Date: 06-2009
Publisher: Springer International Publishing
Date: 05-09-2017
Publisher: IEEE
Date: 11-2008
Publisher: Springer International Publishing
Date: 11-08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-1997
DOI: 10.1109/3477.604113
Abstract: The controller output error method (COEM) is introduced and applied to the design of adaptive fuzzy control systems. The method employs a gradient descent algorithm to minimize a cost function which is based on the error at the controller output. This contrasts with more conventional methods which use the error at the plant output. The cost function is minimized by adapting some or all of the parameters of the fuzzy controller. The proposed adaptive fuzzy controller is applied to the adaptive control of a nonlinear plant and is shown to be capable of providing good overall system performance.
Publisher: ACM
Date: 26-06-2018
Publisher: MDPI AG
Date: 26-11-2018
DOI: 10.3390/TECHNOLOGIES6040110
Abstract: 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.
Publisher: IEEE
Date: 06-2012
Publisher: MDPI AG
Date: 02-10-2021
DOI: 10.3390/ELECTRONICS10192412
Abstract: Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are facing the existing methods of HAR. In HAR, the variations in data required in HAR systems pose challenges to many existing solutions. The type of sensory information used could play an important role in overcoming some of these challenges. Vision-based information in 3D acquired using RGB-D cameras is one type. Furthermore, with the successes encountered in TL, HAR stands to benefit from TL to address challenges to existing methods. Therefore, it is important to review the current state-of-the-art related to both areas. This paper presents a comprehensive survey of vision-based HAR using different methods with a focus on the incorporation of TL in HAR methods. It also discusses the limitations, challenges and possible future directions for more research.
Publisher: MDPI AG
Date: 19-04-2019
DOI: 10.3390/E21040416
Abstract: Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), S le Entropy (S En), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another in idual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.
Publisher: ACM
Date: 05-07-2023
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 03-2009
Publisher: Hindawi Limited
Date: 10-09-2021
DOI: 10.1155/2021/9624386
Abstract: Tremor is a common symptom of Parkinson’s disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and res ling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six res ling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, res ling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE overs ling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.
Publisher: Springer Singapore
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-1998
DOI: 10.1109/72.728403
Abstract: The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.
Publisher: ACM
Date: 07-2015
Publisher: IEEE
Date: 07-2013
DOI: 10.1109/IE.2013.41
Publisher: ACM
Date: 07-2015
Publisher: IEEE
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 07-08-2013
Publisher: Elsevier BV
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-1996
DOI: 10.1109/3477.485885
Abstract: In this paper, a matrix formulation of fuzzy rule based systems is introduced. A gradient descent training algorithm for the determination of the unknown parameters can also be expressed in a matrix form for various adaptive fuzzy networks. When converting a rule-based system to the proposed matrix formulation, only three sets of linear/nonlinear equations are required instead of set of rules and an inference mechanism. There are a number of advantages which the matrix formulation has compared with the linguistic approach. Firstly, it obviates the differences among the various architectures and secondly, it is much easier to organize data in the implementation or simulation of the fuzzy system. The formulation will be illustrated by a number of ex les.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-1996
DOI: 10.1109/3477.485884
Abstract: An adaptive membership function scheme for general additive fuzzy systems is proposed in this paper. The proposed scheme can adapt a proper membership function for any nonlinear input-output mapping, based upon a minimum number of rules and an initial approximate membership function. This parameter adjustment procedure is performed by computing the error between the actual and the desired decision surface. Using the proposed adaptive scheme for fuzzy system, the number of rules can be minimized. Nonlinear function approximation and truck backer-upper control system are employed to demonstrate the viability of the proposed method.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 02-2015
Publisher: IEEE
Date: 12-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: MDPI AG
Date: 27-03-2021
DOI: 10.3390/ELECTRONICS10070793
Abstract: Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming (2) social assistance such as chatting and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.
Publisher: Elsevier BV
Date: 03-2019
Publisher: IEEE
Date: 07-2018
Publisher: MDPI AG
Date: 09-11-2018
DOI: 10.3390/E20110866
Abstract: In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having “consistency” or neatness that enables forklift truck drivers to apply them easily to real shipping containers loaded from one end. Three algorithms are analysed. The first is a basic Monte Carlo Tree Search, driven only by the principle of minimising the length of container that is occupied. The second is an algorithm that uses the proposed entropy measure to drive an otherwise random process. The third algorithm combines these two principles and produces superior results to either. These algorithms are then compared to a classical deterministic algorithm. It is shown that where the classical algorithm fails, the entropy-driven algorithms are still capable of providing good results in a short computational time.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 09-2008
Publisher: Elsevier BV
Date: 09-2004
Publisher: American Scientific Publishers
Date: 05-2017
Publisher: IEEE
Date: 1998
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 09-2008
Publisher: Springer International Publishing
Date: 12-2016
Publisher: MDPI AG
Date: 03-12-2020
DOI: 10.3390/TECHNOLOGIES8040074
Abstract: Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively.
Publisher: IEEE
Date: 04-2011
Publisher: Springer Science and Business Media LLC
Date: 19-07-2018
Publisher: ACM
Date: 05-07-2023
Publisher: Elsevier BV
Date: 10-2019
Publisher: IEEE
Date: 06-2007
Publisher: IEEE
Date: 03-2016
Publisher: Springer International Publishing
Date: 30-08-2020
Publisher: Springer Science and Business Media LLC
Date: 22-12-2022
DOI: 10.1007/S12652-020-02824-Z
Abstract: Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.
Publisher: IEEE
Date: 07-2014
Publisher: International Academy Publishing (IAP)
Date: 11-2008
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 02-2017
Publisher: IEEE
Date: 12-2015
Publisher: ACM
Date: 05-06-2019
Publisher: IEEE
Date: 10-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 07-2017
Publisher: SPIE
Date: 02-10-2008
DOI: 10.1117/12.799831
Publisher: Springer International Publishing
Date: 07-09-2016
Publisher: Wiley
Date: 24-06-2014
DOI: 10.1111/COIN.12045
Publisher: ACM
Date: 05-07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Publisher: ACM
Date: 29-06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-01-2021
Publisher: MDPI AG
Date: 30-07-2020
DOI: 10.3390/E22080845
Abstract: This paper presents anomaly detection in activities of daily living based on entropy measures. It is shown that the proposed approach will identify anomalies when there are visitors representing a multi-occupant environment. Residents often receive visits from family members or health care workers. Therefore, the residents’ activity is expected to be different when there is a visitor, which could be considered as an abnormal activity pattern. Identifying anomalies is essential for healthcare management, as this will enable action to avoid prospective problems early and to improve and support residents’ ability to live safely and independently in their own homes. Entropy measure analysis is an established method to detect disorder or irregularities in many applications: however, this has rarely been applied in the context of activities of daily living. An experimental evaluation is conducted to detect anomalies obtained from a real home environment. Experimental results are presented to demonstrate the effectiveness of the entropy measures employed in detecting anomalies in the resident’s activity and identifying visiting times in the same environment.
Publisher: ACM
Date: 05-06-2019
Publisher: IEEE
Date: 2003
Publisher: ACM
Date: 05-06-2019
Publisher: ACM
Date: 05-06-2019
Publisher: Springer Science and Business Media LLC
Date: 09-08-2022
DOI: 10.1007/S42979-022-01302-X
Abstract: Most studies in the field of search algorithms have only focused on pursuing agents, while comparatively less attention has been paid to target algorithms that employ strategies to evade multiple pursuing agents. In this study, a state-of-the-art target algorithm, TrailMax, has been enhanced and implemented for multiple agent pathfinding problems. The presented algorithm aims to maximise the capture time if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks, measuring the capture cost, the success of escape and statistically analysing the results. The new algorithm, Multiple Pursuers TrailMax, doubles the escaping time steps until capture when compared with existing target algorithms and increases the target’s escaping success by 13% and in some in idual cases by 37%.
Publisher: ACM
Date: 05-06-2019
Publisher: IOP Publishing
Date: 28-05-2012
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 07-2023
Publisher: IEEE
Date: 06-2018
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 2006
Publisher: ACM
Date: 07-2015
Publisher: PeerJ
Date: 05-09-2022
Abstract: Mobile ad-hoc networks (MANETs) and wireless mesh networks (WMNs) are used in a variety of research areas, including the military, industry, healthcare, agriculture, the Internet of Things (IoT), transportation, and smart cities. The swift advancement in MANET technology is the driving force behind this rising adoption rate. Routing over MANET is a critical problem due to the dynamic nature of the link qualities, even when nodes are static. A key challenge in MANETs is the need for an efficient routing protocol that establishes a route according to certain performance metrics related to the link quality. The routing protocols utilised by the nodes in WMNs and MANETs are distinct. Nodes in both types of networks exchange data packets through the routing protocols. For this highly mobile network, the ad-hoc On-Demand Distance Vector (AODV) routing protocol has been suggested as a possible solution. Recent years have attracted researchers’ attention to AODV since it is a routing technique for ad-hoc networks that prevents looping. The architecture of this routing protocol considers several factors, including the mobility of nodes, the failure of connection links, and the loss of packets. In this systematic review, one of the key focuses is bringing attention to the classic AODV, which was developed after discussing the recent development of several versions of AODV. The AODV routing protocol performs a path strength check to generate a more reliable and secure route between the source and destination nodes. In AODV, investigations demonstrate advances in both the format protocol approach and the network simulation-2 (NS-2), and these improvements were made in the same scenario used to revitalise AODV. It has been discovered that the AODV is more effective in several aspects, such as throughput, end-to-end delay, packet delivery ratio (PDR), energy consumption, jitter, packet loss ratio, and network overhead. Furthermore, this paper presents this systematic review based on AODV modifications in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It also provides a methodological framework for the papers’ selection.
Publisher: IEEE
Date: 09-2012
Publisher: Elsevier BV
Date: 11-2016
Publisher: Research Square Platform LLC
Date: 23-04-2020
DOI: 10.21203/RS.3.RS-24215/V1
Abstract: Background Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users’ perspectives in terms of device design and implementation.Objectives This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, needs, and requirements of wearable devices.Materials and Methods This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using a thematic approach.Results It was noted that the well-known assessment and monitoring process such as Unified Parkinson's Disease Rating Scale was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients’ memories. Participants suggested that objective diagnosis and assessment methods are needed to increase the chance of effective treatment. The participants’ perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor.Conclusion The study concluded that current diagnosis and assessment methods are limited. Patients’ and healthcare professionals’ involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.
Publisher: IEEE
Date: 09-2015
Location: United Kingdom of Great Britain and Northern Ireland
Location: Iran (Islamic Republic of)