Topic Editors

1. Department of Computer Science and Engineering, Kuwait College of Science and Technology, Kuwait City, Kuwait
2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada
Dr. Ali Karime
Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON, Canada

Intelligent Health Monitoring and Assistance Systems and Frameworks

Abstract submission deadline
closed (20 June 2023)
Manuscript submission deadline
closed (20 August 2023)
Viewed by
36190

Topic Information

Dear Colleagues,

Healthcare systems and frameworks have evolved over the years and have become significantly autonomous. Healthcare monitoring, assistance, and treatment have greatly benefited from advances in artificial intelligence (AI), computer processing, data storage, communication, and networking. With the recent pandemic came new and innovative solutions for health monitoring and assistance. Drones were used to deliver medicine to remote areas. Image processing and machine learning solutions were used to identify potential viral infections. In-home health assistance was available through advanced IoT-based sensors and devices. Such advances have led researchers and health practitioners to envision new state-of-the-art solutions that rely heavily on AI-based mechanisms. In that context, this Special Issue invites researchers both in academia and industry to submit their original contributions in the area of AI-enabled health monitoring and assistance systems. The topics may include areas in networking, security, machine learning, haptics, modeling, and IoT-based health tools.

Dr. Ismaeel Al Ridhawi
Dr. Ali Karime
Topic Editors

Keywords

  • artificial intelligence
  • electronic health
  • haptics
  • blockchain
  • next-generation networks
  • internet of things

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.3 2011 17.8 Days CHF 2400
Electronics
electronics
2.6 5.3 2012 16.8 Days CHF 2400
Healthcare
healthcare
2.4 3.5 2013 20.5 Days CHF 2700
Journal of Sensor and Actuator Networks
jsan
3.3 7.9 2012 22.6 Days CHF 2000
Sensors
sensors
3.4 7.3 2001 16.8 Days CHF 2600

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (10 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
16 pages, 6254 KiB  
Article
Correlation Analysis of Large-Span Cable-Stayed Bridge Structural Frequencies with Environmental Factors Based on Support Vector Regression
by Jingye Xu, Tugang Xiao, Yu Liu, Yu Hong, Qianhui Pu and Xuguang Wen
Sensors 2023, 23(23), 9442; https://doi.org/10.3390/s23239442 - 27 Nov 2023
Cited by 1 | Viewed by 714
Abstract
The dynamic characteristics of bridge structures are influenced by various environmental factors, and exploring the impact of environmental temperature and humidity on structural modal parameters is of great significance for structural health assessment. This paper utilized the Covariance-Driven Stochastic Subspace Identification method (SSI-COV) [...] Read more.
The dynamic characteristics of bridge structures are influenced by various environmental factors, and exploring the impact of environmental temperature and humidity on structural modal parameters is of great significance for structural health assessment. This paper utilized the Covariance-Driven Stochastic Subspace Identification method (SSI-COV) and clustering algorithms to identify modal frequencies from four months of acceleration data collected from the health monitoring system of the Jintang Hantan Twin-Island Bridge. Furthermore, a correlation analysis is conducted to examine the relationship between higher-order frequency and environmental factors, including temperature and humidity. Subsequently, a Support Vector Machine Regression (SVR) model is employed to analyze the effects of environmental temperature on structural modal frequencies. This study has obtained the following conclusions: 1. Correlation analysis revealed that temperature is the primary influencing factor in frequency variations. Frequency exhibited a strong linear correlation with temperature and little correlation with humidity. 2. SVR regression analysis was performed on frequency and temperature, and an evaluation of the fitting residuals was conducted. The model effectively fit the sample data and provided reliable predictive results. 3. The original structural frequencies underwent smoothing, eliminating the influence of temperature-induced frequency data generated by the SVR model. After eliminating the temperature effects, the fluctuations in frequency within a 24 h period significantly decreased. The data presented in this paper can serve as a reference for further health assessments of similar bridge structures. Full article
Show Figures

Figure 1

24 pages, 2509 KiB  
Article
A Multimodal Software Architecture for Serious Exergames and Its Use in Respiratory Rehabilitation
by Claudinei Dias, Jhonatan Thallisson Cabral Nery, Marcelo da Silva Hounsell and André Bittencourt Leal
Sensors 2023, 23(21), 8870; https://doi.org/10.3390/s23218870 - 31 Oct 2023
Viewed by 1266
Abstract
Serious Exergames (SEGs) have been little concerned with flexibility/equivalence, complementarity, and monitoring (functionalities of systems that deal with a wide variety of inputs). These functionalities are necessary for health SEGs due to the variety of treatments and measuring requirements. No known SEG architectures [...] Read more.
Serious Exergames (SEGs) have been little concerned with flexibility/equivalence, complementarity, and monitoring (functionalities of systems that deal with a wide variety of inputs). These functionalities are necessary for health SEGs due to the variety of treatments and measuring requirements. No known SEG architectures include these three functionalities altogether. In this paper, we present the 123-SGR software architecture for the creation of an SEG that is appropriate to the needs of professionals and patients in the area of rehabilitation. An existing SEG was adapted and therapy-related sensor devices (Pneumotachograph, Manovacuometer, Pressure Belt, and Oximeter) were built to help the patient interact with the SEG. The architecture allows the most varied input combinations, with and without fusion, and these combinations are possible for both conscious and unconscious signals. Health and Technology professionals have assessed the SEG and found that it had the functionalities of flexibility/equivalence, complementarity, and monitoring, and that these are really important and necessary functionalities. The 123-SGR architecture can be used as a blueprint for future SEG development. Full article
Show Figures

Figure 1

27 pages, 2451 KiB  
Article
Short-Range Localization via Bluetooth Using Machine Learning Techniques for Industrial Production Monitoring
by Francesco Di Rienzo, Alessandro Madonna, Nicola Carbonaro, Alessandro Tognetti, Antonio Virdis and Carlo Vallati
J. Sens. Actuator Netw. 2023, 12(5), 75; https://doi.org/10.3390/jsan12050075 - 15 Oct 2023
Cited by 1 | Viewed by 1573
Abstract
Indoor short-range localization is crucial in many Industry 4.0 applications. Production monitoring for assembly lines, for instance, requires fine-grained positioning for parts or goods in order to keep track of the production process and the stations traversed by each product. Due to the [...] Read more.
Indoor short-range localization is crucial in many Industry 4.0 applications. Production monitoring for assembly lines, for instance, requires fine-grained positioning for parts or goods in order to keep track of the production process and the stations traversed by each product. Due to the unavailability of the Global Positioning System (GPS) for indoor positioning, a different approach is required. In this paper, we propose a specific design for short-range indoor positioning based on the analysis of the Received Signal Strength Indicator (RSSI) of Bluetooth beacons. To this aim, different machine learning techniques are considered and assessed: regressors, Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). A realistic testbed is created to collect data for the training of the models and to assess the performance of each technique. Our analysis highlights the best models and the most convenient and suitable configuration for indoor localization. Finally, the localization accuracy is calculated in the considered use case, i.e., production monitoring. Our results show that the best performance is obtained using the K-Nearest Neighbors technique, which results in a good performance for general localization and in a high level of accuracy, 99%, for industrial production monitoring. Full article
Show Figures

Figure 1

12 pages, 441 KiB  
Article
Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller
by Marek Żyliński, Amir Nassibi and Danilo P. Mandic
Sensors 2023, 23(17), 7521; https://doi.org/10.3390/s23177521 - 30 Aug 2023
Cited by 2 | Viewed by 1483
Abstract
At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification [...] Read more.
At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller. We used the CMSIS-DSP library, which supports Naïve Bayes and Support Vector Machine classifiers, with different kernel functions. We also developed Python scripts to automatically transfer the Python model (trained in Scikit-learn) to the C environment. To train and evaluate the models, we used part of the data from the PhysioNet/Computing in Cardiology Challenge 2020 and performed simple classification of atrial fibrillation based on heart-rate irregularity. The performance of the classifiers was tested on a general-purpose ARM Cortex-M4 microcontroller (STM32WB55RG). Our study reveals that among the tested classifiers, the SVM classifier with RBF kernel function achieves the highest accuracy of 96.9%, sensitivity of 98.4%, and specificity of 95.8%. The execution time of this classifier was 720 μs per recording. We also discuss the advantages of moving computing tasks to edge devices, including increased power efficiency of the system, improved patient data privacy and security, and reduced overall system operation costs. In addition, we highlight a problem with false-positive detection and unclear significance of device-detected atrial fibrillation. Full article
Show Figures

Figure 1

23 pages, 6459 KiB  
Article
Decision Support System Proposal for Medical Evacuations in Military Operations
by Piotr Lubkowski, Jaroslaw Krygier, Tadeusz Sondej, Andrzej P. Dobrowolski, Lukasz Apiecionek, Wojciech Znaniecki and Pawel Oskwarek
Sensors 2023, 23(11), 5144; https://doi.org/10.3390/s23115144 - 28 May 2023
Cited by 3 | Viewed by 1757
Abstract
The area of military operations is a big challenge for medical support. A particularly important factor that allows medical services to react quickly in the case of mass casualties is the ability to rapidly evacuation of wounded soldiers from a battlefield. To meet [...] Read more.
The area of military operations is a big challenge for medical support. A particularly important factor that allows medical services to react quickly in the case of mass casualties is the ability to rapidly evacuation of wounded soldiers from a battlefield. To meet this requirement, an effective medical evacuation system is essential. The paper presented the architecture of the electronically supported decision support system for medical evacuation during military operations. The system can also be used by other services such as police or fire service. The system meets the requirements for tactical combat casualty care procedures and is composed of following elements: measurement subsystem, data transmission subsystem and analysis and inference subsystem. The system, based on the continuous monitoring of selected soldiers’ vital signs and biomedical signals, automatically proposes a medical segregation of wounded soldiers (medical triage). The information on the triage was visualized using the Headquarters Management System for medical personnel (first responders, medical officers, medical evacuation groups) and for commanders, if required. All elements of the architecture were described in the paper. Full article
Show Figures

Figure 1

17 pages, 342 KiB  
Study Protocol
Development of Smart Clothing to Prevent Pressure Injuries in Bedridden Persons and/or with Severely Impaired Mobility: 4NoPressure Research Protocol
by Anderson da Silva Rêgo, Guilherme Eustáquio Furtado, Rafael A. Bernardes, Paulo Santos-Costa, Rosana A. Dias, Filipe S. Alves, Alar Ainla, Luisa M. Arruda, Inês P. Moreira, João Bessa, Raul Fangueiro, Fernanda Gomes, Mariana Henriques, Maria Sousa-Silva, Alexandra C. Pinto, Maria Bouçanova, Vânia Isabel Fernande Sousa, Carlos José Tavares, Rochelne Barboza, Miguel Carvalho, Luísa Filipe, Liliana B. Sousa, João A. Apóstolo, Pedro Parreira and Anabela Salgueiro-Oliveiraadd Show full author list remove Hide full author list
Healthcare 2023, 11(10), 1361; https://doi.org/10.3390/healthcare11101361 - 9 May 2023
Cited by 2 | Viewed by 3157
Abstract
Pressure injuries (PIs) are a major public health problem and can be used as quality-of-care indicators. An incipient development in the field of medical devices takes the form of Smart Health Textiles, which can possess innovative properties such as thermoregulation, sensing, and antibacterial [...] Read more.
Pressure injuries (PIs) are a major public health problem and can be used as quality-of-care indicators. An incipient development in the field of medical devices takes the form of Smart Health Textiles, which can possess innovative properties such as thermoregulation, sensing, and antibacterial control. This protocol aims to describe the process for the development of a new type of smart clothing for individuals with reduced mobility and/or who are bedridden in order to prevent PIs. This paper’s main purpose is to present the eight phases of the project, each consisting of tasks in specific phases: (i) product and process requirements and specifications; (ii and iii) study of the fibrous structure technology, textiles, and design; (iv and v) investigation of the sensor technology with respect to pressure, temperature, humidity, and bioactive properties; (vi and vii) production layout and adaptations in the manufacturing process; (viii) clinical trial. This project will introduce a new structural system and design for smart clothing to prevent PIs. New materials and architectures will be studied that provide better pressure relief, thermo-physiological control of the cutaneous microclimate, and personalisation of care. Full article
39 pages, 1795 KiB  
Article
Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
by Caroline Omoanatse Alenoghena, Henry Ohiani Ohize, Achonu Oluwole Adejo, Adeiza James Onumanyi, Emmanuel Esebanme Ohihoin, Aliyu Idris Balarabe, Supreme Ayewoh Okoh, Ezra Kolo and Benjamin Alenoghena
J. Sens. Actuator Netw. 2023, 12(2), 20; https://doi.org/10.3390/jsan12020020 - 2 Mar 2023
Cited by 16 | Viewed by 13926
Abstract
The emergence of the COVID-19 pandemic has increased research outputs in telemedicine over the last couple of years. One solution to the COVID-19 pandemic as revealed in literature is to leverage telemedicine for accessing health care remotely. In this survey paper, we review [...] Read more.
The emergence of the COVID-19 pandemic has increased research outputs in telemedicine over the last couple of years. One solution to the COVID-19 pandemic as revealed in literature is to leverage telemedicine for accessing health care remotely. In this survey paper, we review several articles on eHealth and Telemedicine with emphasis on the articles’ focus area, including wireless technologies and architectures in eHealth, communications protocols, Quality of Service, and Experience Standards, among other considerations. In addition, we provide an overview of telemedicine for new readers. This survey reviews several telecommunications technologies currently being proposed along with their standards and challenges. In general, an encompassing survey on the developments in telemedicine technology, standards, and protocols is presented while acquainting researchers with several open issues. Special mention of the state-of-the-art specialist application areas are presented. We conclude the survey paper by presenting important research challenges and potential future directions as they pertain to telemedicine technology. Full article
Show Figures

Figure 1

17 pages, 3141 KiB  
Article
A Smart Monitoring System for Self-Nutrition Management in Pediatric Patients with Inherited Metabolic Disorders: Maple Syrup Urine Disease (MSUD)
by Haneen Reda Banjar
Healthcare 2023, 11(2), 178; https://doi.org/10.3390/healthcare11020178 - 6 Jan 2023
Cited by 1 | Viewed by 2628
Abstract
A metabolic disorder is due to a gene mutation that causes an enzyme deficiency which leads to metabolism problems. Maple Syrup Urine Disease (MSUD) is one of the most common and severe hereditary metabolic disorders in Saudi Arabia. Patients and families were burdened [...] Read more.
A metabolic disorder is due to a gene mutation that causes an enzyme deficiency which leads to metabolism problems. Maple Syrup Urine Disease (MSUD) is one of the most common and severe hereditary metabolic disorders in Saudi Arabia. Patients and families were burdened by complex and regular dietary therapy menus because of the lack of information on food labels, it was also difficult to keep track of MSUD’s typical diet. The prototype smart plate system proposed in this work may help patients with MSUD and their caregivers better manage the patients’ MSUD diet. The use of knowledge-based, food identification techniques and a device could provide a support tool for self-nutrition management in pediatric patients. The requirements of the system are specified by using questionaries. The design of the prototype is divided into two parts: software (mobile application) and hardware (3D model of the plate). The knowledge-based mobile application contains knowledge, databases, inference, food recognition, food plan, monitor food plan, and user interfaces. The hardware prototype is represented in a 3D model. All the patients agreed that a smart plate system connected to a mobile application could help to track and record their daily diet. A self-management application can help MSUD patients manage their diet in a way that is more pleasant, effortless, accurate, and intelligent than was previously possible with paper records. This could support dietetic professional practitioners and their patients to achieve sustainable results. Full article
Show Figures

Figure 1

24 pages, 7258 KiB  
Article
IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database
by Md. Harun-Ar-Rashid, Oindrila Chowdhury, Muhammad Minoar Hossain, Mohammad Motiur Rahman, Ghulam Muhammad, Salman A. AlQahtani, Mubarak Alrashoud, Abdulsalam Yassine and M. Shamim Hossain
Healthcare 2023, 11(1), 139; https://doi.org/10.3390/healthcare11010139 - 1 Jan 2023
Cited by 5 | Viewed by 3775
Abstract
In recent years, the healthcare system, along with the technology that surrounds it, has become a sector in much need of development. It has already improved in a wide range of areas thanks to significant and continuous research into the practical implications of [...] Read more.
In recent years, the healthcare system, along with the technology that surrounds it, has become a sector in much need of development. It has already improved in a wide range of areas thanks to significant and continuous research into the practical implications of biomedical and telemedicine studies. To ensure the continuing technological improvement of hospitals, physicians now also must properly maintain and manage large volumes of patient data. Transferring large amounts of data such as images to IoT servers based on machine-to-machine communication is difficult and time consuming over MQTT and MLLP protocols, and since IoT brokers only handle a limited number of bytes of data, such protocols can only transfer patient information and other text data. It is more difficult to handle the monitoring of ultrasound, MRI, or CT image data via IoT. To address this problem, this study proposes a model in which the system displays images as well as patient data on an IoT dashboard. A Raspberry Pi processes HL7 messages received from medical devices like an ultrasound machine (ULSM) and extracts only the image data for transfer to an FTP server. The Raspberry Pi 3 (RSPI3) forwards the patient information along with a unique encrypted image data link from the FTP server to the IoT server. We have implemented an authentic and NS3-based simulation environment to monitor real-time ultrasound image data on the IoT server and have analyzed the system performance, which has been impressive. This method will enrich the telemedicine facilities both for patients and physicians by assisting with overall monitoring of data. Full article
Show Figures

Figure 1

26 pages, 12093 KiB  
Article
Sleep Pattern Analysis in Unconstrained and Unconscious State
by Won-Ho Jun, Hyung-Ju Kim and Youn-Sik Hong
Sensors 2022, 22(23), 9296; https://doi.org/10.3390/s22239296 - 29 Nov 2022
Cited by 1 | Viewed by 3212
Abstract
Sleep accounts for one-third of an individual’s life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, [...] Read more.
Sleep accounts for one-third of an individual’s life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, analyzing whether individual sleep patterns guarantee sufficient sleep is necessary. Here, we aimed to acquire information regarding the sleep status of individuals in an unconstrained and unconscious state to consequently classify the sleep state. Accordingly, we collected data associated with the sleep status of individuals, such as frequency of tosses and turns, snoring, and body temperature, as well as environmental data, such as room temperature, humidity, illuminance, carbon dioxide concentration, and ambient noise. The sleep state was classified into two stages: nonrapid eye movement and rapid eye movement sleep, rather than the general four stages. Furthermore, to verify the validity of the sleep state classifications, we compared them with heart rate. Full article
Show Figures

Figure 1

Back to TopTop