A Review of Human–Computer Interaction and Virtual Reality Research Fields in Cognitive InfoCommunications

Katona, József

doi:10.3390/app11062646

Cited by 72 publications

(38 citation statements)

References 31 publications

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“…To facilitate interactions between humans and artificial cognition, a collection of insights is presented on how to integrate and expand human cognitive capabilities with those of surrounding digital technologies. The possibilities for developing HCI are numerous and can be used to a variety of domains, including control systems, image analysis, touch interfaces, motion control, and auditory control, from which certain conclusions were formed [11].…”

Section: Related Workmentioning

confidence: 99%

The Impact of Human and Computer Interaction on eLearning Quality

Alebeisat

Altarawneh²,

Alhalhouli³

et al. 2022

Int. J. Interact. Mob. Technol.

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Over the last few years, particularly during the COVID-19 pandemic, the stage of education has clearly shifted from face-to-face instruction to e-learning. Human-Computer Interaction (HCI) paved the way for developing alternative and advanced methods for implementing the concept of e-learning by identifying what aids educational applications and platforms in visualizing content and thus providing students and their education with easy access in their homes during quarantine and providing them with easy access to educational materials throughout time. Students' impressions of the impact of the human-computer interaction process on the quality of e-learning at Tafila Technical University are discussed in this study. Within one year and four months of the COVID-19 quarantine, students got sufficient experience to apply e-learning. Students responded to a questionnaire that contained closed-ended questions. The responses were analyzed to ascertain the influence of the continual human-computer interaction process. The study's primary objective was to determine the beneficial effect of this contact on the quality of e-learning. Despite their academic and academic performance being greatly impacted by the COVID-19 epidemic, students' positive attitudes toward e-learning have been extremely enthusiastic.

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Section: Related Workmentioning

confidence: 99%

The Impact of Human and Computer Interaction on eLearning Quality

Alebeisat

Altarawneh²,

Alhalhouli³

et al. 2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

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“…Human-computer interaction within XR is a recognized area in emerging trends of cross-domain cognitive infocommunications, where the cognitive capabilities of the modern information and communication technologies and human users are considered and evaluated [25]. Probably the most related development is the MaxWhere (https: //www.maxwhere.com/, accessed on 22 April 2021) platform, which evolved from the VirCA [26] collaborative VR platform.…”

Section: Relation To Cognitive Infocommunications Areamentioning

confidence: 99%

Experimental Performance Evaluation of Enhanced User Interaction Components for Web-Based Collaborative Extended Reality

et al. 2021

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COVID-19-related quarantine measures resulted in a significant increase of interest in online collaboration tools. This includes virtual reality (VR) or, in more general term, extended reality (XR) solutions. Shared XR allows for activities such as presentations, training of personnel or therapy to take place in a virtual space instead of a real one. To make online XR as accessible as possible, a significant effort has been put into the development of solutions that can run directly in web browsers. One of the most recognized solutions is the A-Frame software framework, created by Mozilla VR team and supporting most of the contemporary XR hardware. In addition, an extension called Networked-Aframe allows multiple users to share virtual environments, created using A-Frame, in real time. In this article, we introduce and experimentally evaluate three components that extend the functionality of A-Frame and Networked-Aframe. The first one extends Networked-Aframe with the ability to monitor and control users in a shared virtual scene. The second one implements six degrees of freedom motion tracking for smartphone-based VR headsets. The third one brings hand gesture support to the Microsoft HoloLens holographic computer. The evaluation was performed in a dedicated local network environment with 5, 10, 15 and 20 client computers. Each computer represented one user in a shared virtual scene. Since the experiments were carried out with and without the introduced components, the results presented here can also be regarded as a performance evaluation of A-Frame and Networked-Aframe themselves.

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“…Commonly, gait analysis is essential in a clinical investigations such as fall detection [ 1 ], rehabilitation [ 2 , 3 ], physical therapy [ 4 ], etc., for the well-being of a patient suffering from underlying diseases such as strokes, Parkinson’s, or progressive supranuclear palsy (PSP). Current studies focus on the recent development of human gait rehabilitation therapy based on the state of the brain by employing the brain–computer interface (BCI) system [ 5 , 6 , 7 ]. BCI systems are capable of decoding the cognitive state of a patient to provide feedback to an external device such as a wheelchair, robotic prostheses/orthoses, or muscle simulator by acquiring brain signals from electroencephalographic (EEG), as discussed in these papers [ 6 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Current studies focus on the recent development of human gait rehabilitation therapy based on the state of the brain by employing the brain–computer interface (BCI) system [ 5 , 6 , 7 ]. BCI systems are capable of decoding the cognitive state of a patient to provide feedback to an external device such as a wheelchair, robotic prostheses/orthoses, or muscle simulator by acquiring brain signals from electroencephalographic (EEG), as discussed in these papers [ 6 , 8 , 9 ]. In [ 10 ], the authors utilized EEG-based brain signals for distinguishing between a healthy person and a patient by measuring the level of attention of a person toward his gait.…”

Section: Introductionmentioning

confidence: 99%

A Unified Local–Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors

Das

Meher

Sahoo

2022

Sensors

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Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches.

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A Review of Human–Computer Interaction and Virtual Reality Research Fields in Cognitive InfoCommunications

Cited by 72 publications

References 31 publications

The Impact of Human and Computer Interaction on eLearning Quality

The Impact of Human and Computer Interaction on eLearning Quality

Experimental Performance Evaluation of Enhanced User Interaction Components for Web-Based Collaborative Extended Reality

A Unified Local–Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors

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