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Wearable IoT Applications
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Wearable IoT Applications

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 16721

Special Issue Editors

Prof. Dr. Wan-Young Chung

E-Mail Website
Guest Editor
Department of Artificial Intelligence Convergence, Pukyong National University (PKNU), Busan 48531, Korea
Interests: wearable devices; wearable healthcare; AI-powered sensor-signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Dhananjay Singh

E-Mail Website
Guest Editor
ReSESNE Labs, Department of Electronics Engineering, Hankuk (Korea) University of Foreign Studies (HUFS), Seoul 02450, Republic of Korea
Interests: AI; IoT; smart city; e-healthcare; blockchain; connected vehicles; wireless communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Internet of Things (IoT) technologies are now widely used in many areas to makes people’s lives more convenient. IoT wearable technology has been the focus of many studies on the Internet of Things and wearable sensors. These smart devices are classified according to their features. With these classifications, it is straightforward to research the field of wearable technology. Almost all of these studies report that all devices that will be used in the coming years will be smart devices. Experts developed projects that corroborate these findings.

Research on the Internet of Things aims to raise people’s living standards according to usage areas. Many wearable devices and IoT systems have recently been developed with Internet of Things technologies. With wearable smart devices and applications designed with the Internet of Things, many processes in the following areas can be performed: wearable healthcare, wearable fitness applications, wearable IoT technology at the workplace, smart clothing, smart homes, and smart cities.

Prof. Dr. Wan-Young Chung
Assoc. Prof. Dr. Dhananjay Singh
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

  • wearable devices
  • Internet of Things (IoT)
  • artificial-intelligence-powered IoT application
  • human-centric interaction
  • wearable fitness application
  • wearable IoT technology at workplace
  • smart clothing
  • smart city with wearable IoT technology
  • IoT platforms for wearable devices
  • smart homes with wearable IoT technology
  • communication between wearable devices or in wearable systems
  • energy harvesting for wearable devices
  • secure wearable-device communication
  • wearable healthcare
  • smart-city technologies
  • adaptive technologies
  • intelligent human-centric technologies
  • human–computer interaction by using wearable devices

Published Papers (4 papers)

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Research

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18 pages, 5432 KiB  
Article
Smart Wearables with Sensor Fusion for Fall Detection in Firefighting
by Xiaoqing Chai, Renjie Wu, Matthew Pike, Hangchao Jin, Wan-Young Chung and Boon-Giin Lee
Sensors 2021, 21(20), 6770; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206770 - 12 Oct 2021
Cited by 6 | Viewed by 4857
Abstract
During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the [...] Read more.
During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter’s personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter’s fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively. Full article
(This article belongs to the Special Issue Wearable IoT Applications)
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13 pages, 6852 KiB  
Communication
Detecting Facial Region and Landmarks at Once via Deep Network
by Taehyung Kim, Jiwon Mok and Euichul Lee
Sensors 2021, 21(16), 5360; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165360 - 09 Aug 2021
Cited by 4 | Viewed by 2797
Abstract
For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is [...] Read more.
For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is used. Therefore, we propose a model that can simultaneously detect a face region and a landmark without performing the face detection step before landmark detection. The proposed single-shot detection model is based on the framework of YOLOv3, a one-stage object detection method, and the loss function and structure are altered to learn faces and landmarks at the same time. In addition, EfficientNet-B0 was utilized as the backbone network to increase processing speed and accuracy. The learned database used 300W-LP with 64 facial landmarks. The average normalized error of the proposed model was 2.32 pixels. The processing time per frame was about 15 milliseconds, and the average precision of face detection was about 99%. As a result of the evaluation, it was confirmed that the single-shot detection model has better performance and speed than the previous methods. In addition, as a result of using the COFW database, which has 29 landmarks instead of 64 to verify the proposed method, the average normalization error was 2.56 pixels, which was also confirmed to show promising performance. Full article
(This article belongs to the Special Issue Wearable IoT Applications)
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Review

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26 pages, 12054 KiB  
Review
Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review
by Gang Li and Wan-Young Chung
Sensors 2022, 22(3), 1100; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031100 - 31 Jan 2022
Cited by 17 | Viewed by 4520
Abstract
Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving [...] Read more.
Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers’ drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers’ drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely “detection only (open-loop)” and “management (closed-loop)”, both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same. Full article
(This article belongs to the Special Issue Wearable IoT Applications)
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Other

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18 pages, 2202 KiB  
Project Report
Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
by Sayed-Chhattan Shah
Sensors 2021, 21(22), 7701; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227701 - 19 Nov 2021
Cited by 2 | Viewed by 2656
Abstract
Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to [...] Read more.
Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments. Full article
(This article belongs to the Special Issue Wearable IoT Applications)
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