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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 2025 | Viewed by 7524

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

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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

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Published Papers (3 papers)

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Research

15 pages, 3636 KiB  
Article
Extraction of Features for Time Series Classification Using Noise Injection
by Gyu Il Kim and Kyungyong Chung
Sensors 2024, 24(19), 6402; https://doi.org/10.3390/s24196402 - 2 Oct 2024
Cited by 1 | Viewed by 2308
Abstract
Time series data often display complex, time-varying patterns, which pose significant challenges for effective classification due to data variability, noise, and imbalance. Traditional time series classification techniques frequently fall short in addressing these issues, leading to reduced generalization performance. Therefore, there is a [...] Read more.
Time series data often display complex, time-varying patterns, which pose significant challenges for effective classification due to data variability, noise, and imbalance. Traditional time series classification techniques frequently fall short in addressing these issues, leading to reduced generalization performance. Therefore, there is a need for innovative methodologies to enhance data diversity and quality. In this paper, we introduce a method for the extraction of features for time series classification using noise injection to address these challenges. By employing noise injection techniques for data augmentation, we enhance the diversity of the training data. Utilizing digital signal processing (DSP), we extract key frequency features from time series data through sampling, quantization, and Fourier transformation. This process enhances the quality of the training data, thereby maximizing the model’s generalization performance. We demonstrate the superiority of our proposed method by comparing it with existing time series classification models. Additionally, we validate the effectiveness of our approach through various experimental results, confirming that data augmentation and DSP techniques are potent tools in time series data classification. Ultimately, this research presents a robust methodology for time series data analysis and classification, with potential applications across a broad spectrum of data analysis problems. Full article
(This article belongs to the Special Issue Augmented Reality-Based Navigation System for Healthcare)
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18 pages, 12761 KiB  
Article
Robot-Assisted Augmented Reality (AR)-Guided Surgical Navigation for Periacetabular Osteotomy
by Haoyan Ding, Wenyuan Sun and Guoyan Zheng
Sensors 2024, 24(14), 4754; https://doi.org/10.3390/s24144754 - 22 Jul 2024
Cited by 1 | Viewed by 2231
Abstract
Periacetabular osteotomy (PAO) is an effective approach for the surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip joint and the limited field of view (FoV) during the surgery, it is challenging for [...] Read more.
Periacetabular osteotomy (PAO) is an effective approach for the surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip joint and the limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted, augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 headset, which is a state-of-the-art (SOTA) optical see-through (OST) head-mounted display (HMD). For AR guidance, we propose an optical marker-based AR registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS such that the virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns a bone saw with osteotomy planes planned in preoperative images. Then, it provides surgeons with not only virtual constraints to restrict movement of the bone saw but also AR guidance for visual feedback without sight diversion, leading to higher surgical accuracy and improved surgical safety. Comprehensive experiments were conducted to evaluate both the AR registration accuracy and osteotomy accuracy of the developed navigation system. The proposed AR registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The robotic system achieved an average center translation error of 0.96 ± 0.23 mm, an average maximum distance of 1.31 ± 0.20 mm, and an average angular deviation of 3.77 ± 0.85°. Experimental results demonstrated both the AR registration accuracy and the osteotomy accuracy of the developed system. Full article
(This article belongs to the Special Issue Augmented Reality-Based Navigation System for Healthcare)
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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
Cited by 1 | Viewed by 2269
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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