Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
AI-Driven Internet-of-Thing (AIoT) for E-health Applications
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AI-Driven Internet-of-Thing (AIoT) for E-health Applications

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

Deadline for manuscript submissions: closed (5 November 2024) | Viewed by 8819

Special Issue Editors

Prof. Dr. Kun-chan Lan

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
Interests: wireless sensor network; Internet of Things; telemedicine; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Che-Wei Lin

E-Mail Website
Guest Editor
Department of BioMedical Engineering, National Cheng Kung University, Tainan City 70101, Taiwan
Interests: biosignal snalysis based on artificial intelligence algorithms; development of smart phone healthcare APP; medical appllcation of virtual reality system; medical wearable device design
Prof. Dr. Wan-Jung Chang

E-Mail Website
Guest Editor
Department of Electronic Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
Interests: cloud/IoT systems and applications; protocols for heterogeneous networks; wireless sensor networks/ high-speed networks design and analysis; wearable devices and network systems design; artificial intelligent IoT(AIoT) systems and applications

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) is getting its popularity and expected to have a great impact on our daily lives. However, considering the amount of data coming from millions of connected sensors and devices, the ability to handle such big data in a timely and effective manner will decide whether we can fully enjoy the benefits of IoT. The recent advances in artificial intelligence (AI), such as deep learning technologies, have brought opportunities in overcoming the challenges of IoT development. The integration of AI and IoT is expected to become a trend for many applications. When applying AIoT to healthcare, this enables accurate diagnosis and virtual monitoring of patients to develop a personalized patient experience. For example, with the assistance of AIoT technologies, we can remotely monitor and analyze the vita signs of the patients, alert healthcare professionals timely, to improve clinical outcomes and allow early diagnosis.

This Special Issue will cover all the new challenges and opportunities offered by AIoT for E-health applications

Topics include, but are not limited to, the following:

  • AIoT for telemedicine;
  • AIoT for smart hospital;
  • AIoT for mHealth (mobile health);
  • AIoT for pervasive healthcare;
  • AIoT for patient tele-monitoring;
  • AIoT for deaf and hearing-impaired;
  • AIoT for point-of-care (POC) services;
  • AIoT for patient adherence monitoring.

Prof. Dr. Kun-chan Lan
Dr. Che-Wei Lin
Prof. Dr. Wan-Jung Chang
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

  • AI
  • IoT
  • AIoT
  • E-health

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

28 pages, 1868 KiB  
Article
Smart Autism Spectrum Disorder Learning System Based on Remote Edge Healthcare Clinics and Internet of Medical Things
by Mazin Abed Mohammed, Saleh Alyahya, Abdulrahman Abbas Mukhlif, Karrar Hameed Abdulkareem, Hassen Hamouda and Abdullah Lakhan
Sensors 2024, 24(23), 7488; https://doi.org/10.3390/s24237488 - 24 Nov 2024
Viewed by 263
Abstract
Autism spectrum disorder (ASD) is a brain disorder causing issues among many young children. For children suffering from ASD, their learning ability is typically slower when compared to normal children. Therefore, many technologies aiming to teach ASD children with optimized learning approaches have [...] Read more.
Autism spectrum disorder (ASD) is a brain disorder causing issues among many young children. For children suffering from ASD, their learning ability is typically slower when compared to normal children. Therefore, many technologies aiming to teach ASD children with optimized learning approaches have emerged. With this motivation, this study presents a smart autism spectrum disorder learning system based on remote edge healthcare clinics and the Internet of Medical Things, the objective of which is to offer an online education and healthcare environment for autistic children. Concave and convex optimization constraints, such as accuracy, learning score, total processing time with deadline, and resource failure, are considered in the proposed system, with a focus on different autism education learning applications (e.g., speaking, reading, writing, and listening), while respecting the system’s quality of service (QoS) requirements. All of the autism applications are executed on smartwatches, mobile devices, and edge healthcare nodes during their training and analysis in the system. This study presents the smartwatch autism spectrum data learning scheme (SM-ASDS), which consists of different offloading approaches, training analyses, and schemes. The SM-ASDS algorithm methodology includes partitioning offloading and deep convolutional neural network (DCNN)- and adaptive long short-term memory (ALSTM)-based schemes, which are used to train autism-related data on different nodes. The simulation results show that SM-ASDS improved the learning score by 30%, accuracy by 98%, and minimized the total processing time by 33%, when compared to baseline methods. Overall, this study presents an education learning system based on smartwatches for autistic patients, which facilitates educational training for autistic patients based on the use of artificial intelligence techniques. Full article
(This article belongs to the Special Issue AI-Driven Internet-of-Thing (AIoT) for E-health Applications)
27 pages, 3793 KiB  
Article
Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT
by Muhammad Nafees Ulfat Khan, Zhiling Tang, Weiping Cao, Yawar Abbas Abid, Wanghua Pan and Ata Ullah
Sensors 2023, 23(18), 7799; https://doi.org/10.3390/s23187799 - 11 Sep 2023
Cited by 6 | Viewed by 1753
Abstract
The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors [...] Read more.
The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots. Full article
(This article belongs to the Special Issue AI-Driven Internet-of-Thing (AIoT) for E-health Applications)
►▼ Show Figures

Figure 1

Review

Jump to: Research

23 pages, 2328 KiB  
Review
IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring
by Fraser Young, Rachel Mason, Rosie E. Morris, Samuel Stuart and Alan Godfrey
Sensors 2023, 23(8), 4100; https://doi.org/10.3390/s23084100 - 19 Apr 2023
Cited by 6 | Viewed by 5569
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation [...] Read more.
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment. Full article
(This article belongs to the Special Issue AI-Driven Internet-of-Thing (AIoT) for E-health Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop