IoT-driven augmented reality and virtual reality systems in neurological sciences

Sahu, Mehar; Gupta, Rohan; Ambasta, Rashmi K.; Kumar, Pravir

doi:10.1016/j.iot.2024.101098

Cited by 7 publications

(1 citation statement)

References 228 publications

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“…The merging of SLAM with XR technologies indicates a growing new area of improving spatial awareness, interactive experiences, and developing highly immersive digital environments. Moreover, the incorporation of control and estimation methods into many area [15][16][17][18][19][20][21][22][23][24][25][26][27][28], including into SLAM algorithms further enhances accuracy and reliability in dynamic environments, bridging the gap between theoretical modeling and real-world applications [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. The progression of SLAM, starting with its basic algorithms and extending to its use in XR, signifies a crucial moment in the story of computer sciences and human-computer interaction [45][46][47][48][49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

SLAM in XR: a bibliometric analysis of its application in virtual, augmented, and mixed reality

Obaideen,

AlShabi

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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The growing sector of Extended Reality (XR), comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), requires successful approaches for localization and mapping. Simultaneous Localization and Mapping (SLAM) has gained more importance in XR uses. This research conducts an in-depth bibliometric examination of the utilization of SLAM in XR settings, analyzing publications from the last ten years. We investigate the developing trends, fields of knowledge, prominent contributors, and important publications that have influenced this intersection. Our study indicates a notable increase in research on incorporating SLAM in XR, especially in fields such as gaming, architectural visualization, and remote collaboration. A study of the region highlights North America and the Asia-Pacific as key players in this field of research. The research also discusses the difficulties involved in integrating SLAM into XR, such as handling the computational requirements, guaranteeing real-time processing, and accomplishing successful sensor fusion. This paper strives to provide a thorough overview of the current status of SLAM in XR, pinpointing gaps that already exist and suggesting directions for further research. These observations aim to help researchers, developers, and industry professionals who want to use SLAM's capabilities in the quickly changing XR field.

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Section: Introductionmentioning

confidence: 99%

SLAM in XR: a bibliometric analysis of its application in virtual, augmented, and mixed reality

Obaideen,

AlShabi

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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Decoding educational augmented reality research trends: a topic modeling analysis

Ozyurt,

Ozyurt

2024

Educ Inf Technol

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This study aims to examine the temporal evolution and changes of research interests and trends in the educational augmented reality (AR) literature. To this end, 3718 articles published in the 2003–2022 period and indexed in the Scopus database were analyzed through machine learning-based semantic topic modeling and descriptive analysis. The findings indicate a notable upswing in studies on educational AR, particularly since 2015. The articles were categorized into eleven primary themes through topic modeling analysis. The three most prevalent topics in terms of volume are “Augmented Reality in Education and Cultural Heritage”, “Medical Education and Patient Care”, and “Enhancing Safety and Information in Food Consumption”. Observations across different times reveal that “Augmented Reality in Electrical and Electronic Systems” and “Gesture-Based Instruction and Maintenance” were studied in the initial periods. Since 2015, there has been a notable increase in applications falling under the “Serious Games” category. The least voluminous and slowest-evolving topics are identified as “Serious Games for Children with Autism Spectrum Disorder”, “Augmented Reality in Chemistry and Biology Laboratories”, and “Augmented Reality for Safe and Efficient Driving”. Considering the recent momentum gained by these topics, it is anticipated that they will become popular topics for future studies. This study represents a significant milestone as the first and most comprehensive research using machine learning in its field, not only explaining the current state of the field but also providing valuable information for future research efforts.

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Order Matters: The Effect of “AR-First” or “AR-Later” on Consumer Decision-Making

Sun,

Wang,

Liu

2024

International Journal of Human–Computer Interaction

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IoT-driven augmented reality and virtual reality systems in neurological sciences

Cited by 7 publications

References 228 publications

SLAM in XR: a bibliometric analysis of its application in virtual, augmented, and mixed reality

SLAM in XR: a bibliometric analysis of its application in virtual, augmented, and mixed reality

Decoding educational augmented reality research trends: a topic modeling analysis

Order Matters: The Effect of “AR-First” or “AR-Later” on Consumer Decision-Making

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