An integrated semantic framework for designing context-aware Internet of Robotic Things systems

Sabri, Lyazid; Bouznad, Sofiane; Fiorini, Sandro Rama; Chibani, Abdelghani; Prestes, Edson; Amirat, Yacine

doi:10.3233/ica-170559

Cited by 17 publications

(12 citation statements)

References 21 publications

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“…Brain–computer interface (BCI) systems provide a communication and control path between a computer and a human with applications in diverse areas such as medical (Burns et al, ; Yin et al, ; Sereshkeh et al, ), robotics (Lu et al, ; Chen et al, ; Sabri et al, ), the military, and games. Development of BCI systems requires a multidisciplinary approach crossing many fields including neurobiology, psychology, engineering, mathematics, and computer science that is highly affected by developments in each of those fields (Mirzaei et al, ; Mirzaei and Adeli, ; Jiao et al, ).…”

Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

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Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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“…[37][38][39][40] Actuation and control methods assist IoRT physical and virtual devices across monitoring and managing context-aware perception and modeling systems by use of multi-agent systems, cloud computing technologies, and failure checking techniques. [41][42][43][44] Remote robotic cooperation and streaming workflow optimize computer simulation and modeling of data sharing processes through networked cloud robotics, robot clusters, and heuristic algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The objective of this systematic review was to analyze the recently published literature on IoRT and integrate the insights it articulates on big data management algorithms [37][38][39][40], deep learning-based object detection technologies [41][42][43][44], and geospatial simulation and sensor fusion tools [45][46][47][48]. Cooperative unmanned and decentralized tracking systems require mobile clustering algorithms to optimize sensing capabilities.…”

Section: Introductionmentioning

confidence: 99%

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

Andronie

Lăzăroiu

Iatagan

et al. 2023

IJGI

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The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.

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“…Internet of Things (IoT) technology can connect wearables to AI based capability on the web leveraging approaches such as the W3C Web of Things. 7 The integration of Robotics, IoT, Web and Cloud technologies paves the way to the Internet of Robotics Things (IoRT) 8 [ 94 , 98 , 106 , 115 ], i.e. an environment where unlimited and continuously improving knowledge reasoning and data processing is available, thereby endowing robots with capabilities for context awareness and for interactions with humans by reacting to their emotions, thus enhancing the quality of human-robot interactions.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being

Gyrard

Tabeau

Fiorini

et al. 2021

Int J of Soc Robotics

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An integrated semantic framework for designing context-aware Internet of Robotic Things systems

Cited by 17 publications

References 21 publications

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being

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