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Artificial Intelligence and Internet of Things in Healthcare Systems

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 27381

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Special Issue Editors

Prof. Dr. Hiroyuki Tomiyama

E-Mail Website
Guest Editor

Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-0058, Japan
Interests: embedded and cyber-physical systems; electronic design automation and optimization; autonomous drones; biochip synthesis
Special Issues, Collections and Topics in MDPI journals

Dr. Lin Meng

E-Mail Website
Guest Editor

Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Japan
Interests: processor architecture; high-performance computing; AI-based IoT; underwater drones; cultural heritage preservation and protection
Special Issues, Collections and Topics in MDPI journals

Dr. Kenshi Saho

E-Mail Website
Guest Editor

Department of Intelligent Robotics, Toyama Prefectural University, Toyama, Japan
Interests: signal processing for micro-Doppler radar sensing; smart sensor fusion system for remote monitoring systems; environmental information measurement

Dr. Xiangbo Kong

E-Mail Website
Guest Editor

Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-0058, Japan
Interests: embedded systems; image processing in healthcare; IoT systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of healthcare is to promote a better quality of life. The traditional healthcare industry is facing numerous challenges today, including an aging society, telehealth, and a high labor cost. The development of Artificial Intelligence (AI) and the Internet of Things (IoT) also pushes the horizon of healthcare.

Sensing and image processing are widely used in healthcare, and AI greatly improves the accuracy of these methods. On the other hand, it is rather challenging to apply AI to healthcare because of the high computing cost. Additionally, there remains much room for further research because extremely high accuracy is required in healthcare. IoT reduces the computing cost of AI and makes healthcare more convenient. However, edge-cloud optimization and security in IoT systems remain significant challenges. In light of these potentials, this Special Issue solicits original research which advances accuracy, computing efficiency, and security in healthcare. Surveys and reviews are also welcomed.

This Special Issue is an open call but also invites selected papers from the third International Symposium on Advanced Technologies and Applications on the Internet of Things (ATAIT 2021, ATAIT2022 and ATAIT 2023).

Prof. Dr. Hiroyuki Tomiyama
Dr. Lin Meng
Dr. Kenshi Saho
Dr. Xiangbo Kong
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-enhanced healthcare services
IoT-enabled healthcare services
Embedded system in healthcare
AI hardware in healthcare
Privacy and security in healthcare
Human sensing in healthcare
Human behavior modeling and analysis in healthcare
Parallel processing/pipeline processing in healthcare
Image processing for public health
Biological and biomedical image processing

Published Papers (9 papers)

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Research

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19 pages, 1195 KiB

Open AccessArticle

An Adaptive Intrusion Detection System in the Internet of Medical Things Using Fuzzy-Based Learning

by Mousa Alalhareth and Sung-Chul Hong

Sensors 2023, 23(22), 9247; https://0-doi-org.brum.beds.ac.uk/10.3390/s23229247 - 17 Nov 2023

Cited by 1 | Viewed by 1193

Abstract

The Internet of Medical Things (IoMT) is a growing trend within the rapidly expanding Internet of Things, enhancing healthcare operations and remote patient monitoring. However, these devices are vulnerable to cyber-attacks, posing risks to healthcare operations and patient safety. To detect and counteract attacks on the IoMT, methods such as intrusion detection systems, log monitoring, and threat intelligence are utilized. However, as attackers refine their methods, there is an increasing shift toward using machine learning and deep learning for more accurate and predictive attack detection. In this paper, we propose a fuzzy-based self-tuning Long Short-Term Memory (LSTM) intrusion detection system (IDS) for the IoMT. Our approach dynamically adjusts the number of epochs and utilizes early stopping to prevent overfitting and underfitting. We conducted extensive experiments to evaluate the performance of our proposed model, comparing it with existing IDS models for the IoMT. The results show that our model achieves high accuracy, low false positive rates, and high detection rates, indicating its effectiveness in identifying intrusions. We also discuss the challenges of using static epochs and batch sizes in deep learning models and highlight the importance of dynamic adjustment. The findings of this study contribute to the development of more efficient and accurate IDS models for IoMT scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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18 pages, 2832 KiB

Open AccessArticle

Managing Security of Healthcare Data for a Modern Healthcare System

by Abdulmohsen Almalawi, Asif Irshad Khan, Fawaz Alsolami, Yoosef B. Abushark and Ahmed S. Alfakeeh

Sensors 2023, 23(7), 3612; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073612 - 30 Mar 2023

Cited by 18 | Viewed by 4445

Abstract

The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals’ access to cloud-based patient-sensitive data more securely. The experiment’s findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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14 pages, 5788 KiB

Open AccessArticle

Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method

by Vidas Raudonis, Arturas Kairys, Rasa Verkauskiene, Jelizaveta Sokolovska, Goran Petrovski, Vilma Jurate Balciuniene and Vallo Volke

Sensors 2023, 23(7), 3431; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073431 - 24 Mar 2023

Cited by 3 | Viewed by 1800

Abstract

In this study, a novel method for automatic microaneurysm detection in color fundus images is presented. The proposed method is based on three main steps: (1) image breakdown to smaller image patches, (2) inference to segmentation models, and (3) reconstruction of the predicted segmentation map from output patches. The proposed segmentation method is based on an ensemble of three individual deep networks, such as U-Net, ResNet34-UNet and UNet++. The performance evaluation is based on the calculation of the Dice score and

I o U

values. The ensemble-based model achieved higher Dice score (0.95) and

I o U

(0.91) values compared to other network architectures. The proposed ensemble-based model demonstrates the high practical application potential for detection of early-stage diabetic retinopathy in color fundus images. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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16 pages, 3942 KiB

Open AccessArticle

Kalman-Filter-Based Learning of Characteristic Profiles of Lithium-Ion Batteries

by Masahito Arima, Lei Lin and Masahiro Fukui

Sensors 2022, 22(14), 5156; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145156 - 09 Jul 2022

Cited by 1 | Viewed by 1138

Abstract

The main analyzed aspect of lithium-ion battery (LIB) degradation so far has been capacity fading. On the other hand, interest in efficiency degradation has also increased in recent years. Battery aggregation, which is expected to absorb the surplus of variable renewable energies such as photovoltaic energy, is affected by efficiency degradation in terms of the decreases in the economic gain and renewable energy use. Reusable LIBs could be used as aggregation components in the future; naturally, the variety of charge–discharge efficiencies might be more complex. To improve the operation efficiency of aggregation, including that obtained using reusable LIBs, we propose the Kalman-filter-based quasi-unsupervised learning of the characteristic profiles of LIBs. This method shows good accuracy in the estimation of charge–discharge energy. It should be emphasized that there are no reports of charge–discharge energy estimation using the Kalman filter. In addition, this study shows that the incorrect open-circuit voltage function for the state of charge, which is assumed in the case of a reused battery, could be applied as the reference for the Kalman filter for LIB state estimation. In summary, it is expected that this diagnosis method could contribute to the economic and renewable energy usage improvement of battery aggregation. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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17 pages, 21230 KiB

Open AccessArticle

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

by Tomoyasu Shimada, Hiroki Nishikawa, Xiangbo Kong and Hiroyuki Tomiyama

Sensors 2022, 22(6), 2097; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062097 - 08 Mar 2022

Cited by 12 | Viewed by 4089

Abstract

In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix generates depth images from a monocular camera. Additionally, this work applies optical flow to enhance the accuracy of depth estimation. In this work, we propose a highly accurate depth estimation method that effectively embeds an optical flow map into a monocular image. The models are trained with taking advantage of AirSim, which is one of the flight simulators. AirSim can take both monocular and depth images over a hundred meter in the virtual environment, and our model generates a depth image that provides the long-distance information than images captured by a common depth camera. We evaluate accuracy and error of our proposed method using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance. As a result, our proposed method is higher accuracy and lower error than a state of work. Moreover, our proposed method is lower collision than a state of work. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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12 pages, 3683 KiB

Open AccessCommunication

Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults

by Kenshi Saho, Masahiro Fujimoto, Yoshiyuki Kobayashi and Michito Matsumoto

Sensors 2022, 22(3), 930; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030930 - 25 Jan 2022

Cited by 5 | Viewed by 2453

Abstract

In a previous study, we developed a classification model to detect fall risk for elderly adults with a history of falls (fallers) using micro-Doppler radar (MDR) gait measurements via simulation. The objective was to create daily monitoring systems that can identify elderly people with a high risk of falls. This study aimed to verify the effectiveness of our model by collecting actual MDR data from community-dwelling elderly people. First, MDR gait measurements were performed in a community setting, and the efficient gait parameters for the classification of fallers were extracted. Then, a support vector machine model that was trained and validated using the simulated MDR data was tested for the gait parameters extracted from the actual MDR data. A classification accuracy of 78.8% was achieved for the actual MDR data. The validity of the experimental results was confirmed based on a comparison with the results of our previous simulation study. Thus, the practicality of the faller classification model constructed using the simulated MDR data was verified for the actual MDR data. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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17 pages, 810 KiB

Open AccessArticle

Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level

by Hao Sun, Jiaqing Liu, Shurong Chai, Zhaolin Qiu, Lanfen Lin, Xinyin Huang and Yenwei Chen

Sensors 2021, 21(14), 4764; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144764 - 12 Jul 2021

Cited by 23 | Viewed by 4897

Abstract

Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous machine learning methods have been proposed for estimating the levels of depression via audio, visual, and audiovisual emotion sensing, several challenges still exist. For example, it is difficult to extract long-term temporal context information from long sequences of audio and visual data, and it is also difficult to select and fuse useful multi-modal information or features effectively. In addition, how to include other information or tasks to enhance the estimation accuracy is also one of the challenges. In this study, we propose a multi-modal adaptive fusion transformer network for estimating the levels of depression. Transformer-based models have achieved state-of-the-art performance in language understanding and sequence modeling. Thus, the proposed transformer-based network is utilized to extract long-term temporal context information from uni-modal audio and visual data in our work. This is the first transformer-based approach for depression detection. We also propose an adaptive fusion method for adaptively fusing useful multi-modal features. Furthermore, inspired by current multi-task learning work, we also incorporate an auxiliary task (depression classification) to enhance the main task of depression level regression (estimation). The effectiveness of the proposed method has been validated on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge) in terms of the PHQ-8 scores. Experimental results indicate that the proposed method achieves better performance compared with currently state-of-the-art methods. Our proposed method achieves a concordance correlation coefficient (CCC) of 0.733 on AVEC 2019 which is 6.2% higher than the accuracy (CCC = 0.696) of the state-of-the-art method. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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11 pages, 1190 KiB

Open AccessCommunication

Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

by Sora Hayashi, Kenshi Saho, Keitaro Shioiri, Masahiro Fujimoto and Masao Masugi

Sensors 2021, 21(11), 3643; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113643 - 24 May 2021

Cited by 4 | Viewed by 2161

Abstract

To develop a daily monitoring system for early detection of fall risk of elderly people during walking, this study presents a highly accurate micro-Doppler radar (MDR)-based gait classification method for the young and elderly adults. Our method utilizes a time-series of velocity corresponding to leg motion during walking extracted from the MDR spectrogram (time-velocity distribution) in an experimental study involving 300 participants. The extracted time-series was inputted to a long short-term memory recurrent neural network to classify the gaits of young and elderly participant groups. We achieved a classification accuracy of 94.9%, which is significantly higher than that of a previously presented velocity-parameter-based classification method. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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Review

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21 pages, 1345 KiB

Open AccessReview

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

by Andrzej W. Przybyszewski, Albert Śledzianowski, Artur Chudzik, Stanisław Szlufik and Dariusz Koziorowski

Sensors 2023, 23(4), 2145; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042145 - 14 Feb 2023

Cited by 9 | Viewed by 2918

Abstract

Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer’s (AD) and Parkinson’s diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Healthcare Systems)

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