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Augmented Reality-Based Navigation System for Healthcare
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Augmented Reality-Based Navigation System for Healthcare

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 778

Special Issue Editors

Prof. Dr. Kyungyong Chung

E-Mail Website
Guest Editor
Division of AI Computer Science and Engineering, Kyonggi University, Suwon, Republic of Korea
Interests: AR; artificial intelligence; data mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hoill Jung

E-Mail Website
Guest Editor
Department of Computer & Software Engineering, Daelim University, Anyang, Republic of Korea
Interests: data mining

Special Issue Information

Dear Colleagues,

The development paradigm in AI is shifting towards explainable models that can analyze the basis of predictions, rather than focusing solely on accuracy. This approach processes large amounts of data and provides reliable information. AR (augmented reality) is an extension of the real world by overlapping digital information on the real space. One of the advantages of AR is that it can maximize the user experience. It can be used for wayfinding in conjunction with location information, or can be usefully applied in areas such as education and training, healthcare, and tourism.

Prof. Dr. Kyungyong Chung
Prof. Dr. Hoill Jung
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. Sensors 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 2600 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

  • convergence of the digital (virtual) and physical (real) worlds
  • interface for identifying spaces or objects
  • interaction with objects and artificial intelligence
  • expansion of user experience, explainable model
  • metaverse, data mining, healthcare, augmented reality

Published Papers (1 paper)

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Research

15 pages, 2682 KiB  
Article
Workout Classification Using a Convolutional Neural Network in Ensemble Learning
by Gi-Seung Bang and Seung-Bo Park
Sensors 2024, 24(10), 3133; https://doi.org/10.3390/s24103133 - 15 May 2024
Viewed by 460
Abstract
To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizing MediaPipe, the proposed system extracts [...] Read more.
To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizing MediaPipe, the proposed system extracts the joint coordinates and angles of the human body, which the CNN uses to learn the complex patterns of various exercises. Additionally, this new approach enhances classification performance by combining predictions from multiple image frames using an ensemble learning method. Infinity AI’s Fitness Basic Dataset is employed for validation, and the experiments demonstrate high accuracy in classifying exercises such as arm raises, squats, and overhead presses. The proposed model demonstrated its ability to effectively classify exercise postures in real time, achieving high rates in accuracy (92.12%), precision (91.62%), recall (91.64%), and F1 score (91.58%). This indicates its potential application in personalized fitness recommendations and physical therapy services, showcasing the possibility for beneficial use in these fields. Full article
(This article belongs to the Special Issue Augmented Reality-Based Navigation System for Healthcare)
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