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Electronics
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Augmented Reality in IoT
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Augmented Reality in IoT

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6197

Special Issue Editors

Prof. Dr. Byeong-hee Roh

E-Mail Website
Guest Editor
Department of Software & Computer Engineering, Ajou University, Suwon-si 443-749, Republic of Korea
Interests: multimedia IoT networking and applications
Prof. Dr. SJ Kim

E-Mail Website
Guest Editor
Entertainment Engineering & Design (EED) , University of Nevada Las Vegas, Las Vegas, NV 89154-4022, USA
Interests: augmented human

Special Issue Information

Dear Colleagues,

Augmented Reality (AR) and the Internet of Things (IoT) keep making substantial progress while offering promising research and business opportunities in various fields, such as manufacturing, healthcare, safety, energy, real estate, marketing and tourism. The IoT provides organizations with real time data tracking and the processing of devices, while, on the other hand, AR provides users with a natural and direct interface, allowing them to interact with invisible data. We conjecture that AR with IoT integrations, or vice versa, would accelerate building platform solutions for current and anticipated future challenges that are caused by the gap existing between device data and human perception. IoT platforms embedded within AR would provide a seamless interactivity for their users, while AR platforms combined with IoT connected data sources would provide practical marketable applications.

This Special Issue will solicit state-of-the-art techniques and solutions and also market impactable applications in the area of IoT/AR platforms with the goal of providing a forum for researchers, professionals, and practitioners to disseminate their latest research results and findings.

This Special Issue covers all aspects of the IoT and AR. Topics of interest include, but are not limited to:

  • AR-assisted IoT/IoT-assisted AR
  • Integration and synchronization in IoT/AR
  • Competence, performance, and usability assessment in IoT/AR platforms
  • Physiological and psychological impacts of IoT/AR systems
  • Multisensory data analysis in the platforms
  • Ergonomic approaches of IoT/AR hardware devices
  • Ergonomic approaches of IoT/AR software interfaces
  • Case studies/Empirical studies
  • Applications/Platform design and construction

Prof. Dr. Byeong-hee Roh
Prof. Dr. S.J. Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AR-assisted IoT/IoT-assisted AR
  • Integration and synchronization in IoT/AR
  • Competence, performance, and usability assessment in IoT/AR platforms
  • Physiological and psychological impacts of IoT/AR systems
  • Multisensory data analysis in the platforms
  • Ergonomic approaches of IoT/AR hardware devices
  • Ergonomic approaches of IoT/AR software interfaces
  • Case studies/Empirical studies
  • Applications/Platform design and construction

Published Papers (2 papers)

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Research

14 pages, 2088 KiB  
Article
Motor Indicators for the Assessment of Frozen Shoulder Rehabilitation via a Virtual Reality Training System
by Si-Huei Lee, Shih-Ching Yeh, Jianjun Cui, Chia-Ru Chung, Chang-Hsin Yeh and Lizheng Liu
Electronics 2021, 10(6), 740; https://doi.org/10.3390/electronics10060740 - 20 Mar 2021
Cited by 3 | Viewed by 2755
Abstract
Adhesive capsulitis (also known as frozen shoulder) is a common clinical shoulder disorder and can be effectively improved through physical rehabilitation. With advancements in technology, virtual reality (VR) has been increasingly employed in rehabilitation treatments. However, most relevant studies have merely employed traditional [...] Read more.
Adhesive capsulitis (also known as frozen shoulder) is a common clinical shoulder disorder and can be effectively improved through physical rehabilitation. With advancements in technology, virtual reality (VR) has been increasingly employed in rehabilitation treatments. However, most relevant studies have merely employed traditional assessment tools to assess the therapeutic effects rather than the substantial amount of motor trajectory data or task performance collected by motor training systems. In this research, an innovative frozen shoulder rehabilitation system using a Microsoft Kinect sensor and VR was successfully developed and five task-oriented motor indices and task performance were proposed to assess motor performance. A clinical experiment involving twenty patients was conducted. Objective clinical assessment outcomes verified the effectiveness of the developed system for frozen shoulder rehabilitation. The improvements assessed according to motor indices and task performance were consistent with the objective clinical assessment results. Furthermore, correlation analysis showed that several items in the task performance and motor indices were significantly correlated to clinical assessment items. Moreover, numerous items in the task performance and motor indices capable of predicting the clinical assessment results were identified through stepwise regression analysis. The results of this research can facilitate the subsequent development of new assessment methods. Full article
(This article belongs to the Special Issue Augmented Reality in IoT)
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16 pages, 4048 KiB  
Article
Design of Mirror Therapy System Base on Multi-Channel Surface-Electromyography Signal Pattern Recognition and Mobile Augmented Reality
by Lizheng Liu, Jianjun Cui, Jian Niu, Na Duan, Xianjia Yu, Qingqing Li, Shih-Ching Yeh and Li-Rong Zheng
Electronics 2020, 9(12), 2142; https://doi.org/10.3390/electronics9122142 - 14 Dec 2020
Cited by 3 | Viewed by 2296
Abstract
Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and [...] Read more.
Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern recognition and mobile augmented reality is studied. Besides, wavelet transform method is designed to mitigate the noise. The spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Two approaches, including Convolutional Neural Network (CNN) and grid-optimized Support Vector Machine (SVM), are designed to classify the SEMG of different gestures. The mobile augmented reality provides a virtual hand movement in the real environment to perform mirror therapy process. The experimental results show that the overall accuracy of SVM is 93.07%, and that of CNN is up to 97.8%. Full article
(This article belongs to the Special Issue Augmented Reality in IoT)
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