Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients

Rahman, Muhammad Zia Ur; Raza, Ali; Alsanad, Abeer Abdul‐Aziz; Akbar, Muhammad Azeem; Liaquat, Rabia; Riaz, Muhammad Tanveer; AlSuwaidan, Lulwah; Al-Alshaikh, Halah Abdulaziz; AlSagri, Hatoon S.

doi:10.3934/mbe.2022357

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…The author in [52] offer a Smart Health Monitoring System framework built using IoT and machine learning to monitor patients affected by coronary artery disease. Research by [53] focuses on developing an AI-based patient monitoring system to observe the vital signs of Covid 19 patients remotely. Another research team in [54] have developed a smart healthcare framework for the detection and monitoring of COVID-19 using smart connectivity sensors and deep learning (DL).…”

Section:  Mobile and Wearable Health Monitoring Systemsmentioning

confidence: 99%

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Hassan¹,

Sulaiman²,

Abdulgabber³

et al. 2023

IJACSA

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In recent years, the integration of artificial intelligence (AI) technologies has greatly benefited smart city healthcare, meeting the growing demand for affordable, efficient, and real-time healthcare services. Patient monitoring is one area where artificial intelligence has shown great promise. Improved health outcomes have been made possible by the advancement of AI-based monitoring systems, which enable more personalized and continuous patient monitoring. However, to fully maximize the benefits of these systems, a user-centered approach is essential, which prioritizes patients' needs and experiences while ensuring their privacy and autonomy are respected. This study focuses on the application of user-centered design principles in the development and deployment of AI-driven monitoring systems in smart city healthcare. Addressing the challenges and opportunities of AI-driven monitoring systems, the article considers issues such as privacy and security concerns, data accuracy, and user acceptance. Finally, some possible future directions to the challenges are suggested. A user-centered approach to AI monitoring systems is recommended for healthcare providers to enhance patient experience in smart city healthcare.

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Section:  Mobile and Wearable Health Monitoring Systemsmentioning

confidence: 99%

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Hassan¹,

Sulaiman²,

Abdulgabber³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Investigation into Smart Healthcare Monitoring System in an IoT Environment was proposed in Salini et al, (2022), The limitation is that the system uses Raspberry Pi as the main processor which is expensive. Real-time artificial intelligence-based health monitoring, diagnosing and environmental control system for COVID-19 patients was developed by Muhammad et al, (2022). The aim was to allow doctors and families to track a patient's health outside the hospital using sensors, cloud storage, data transmission, and IoT mobile applications.…”

Section: Introductionmentioning

confidence: 99%

“…The aim was to allow doctors and families to track a patient's health outside the hospital using sensors, cloud storage, data transmission, and IoT mobile applications. Limitations point at allowing poorly trained AI system to inject a patient without a doctor's proper review and approval likewise non clarity of the symptoms (Zia Ur Rahman et al, 2022). An IoT-Based Smart Health Monitoring System for COVID-19 patients was developed by Vaneeta et al, (2022), the major limitation of this system is security, as data spoofing is easy.…”

Section: Introductionmentioning

confidence: 99%

Cushy and Cheap Smart Health Monitoring System Incorporating a Ventilator

Okomba,

Esan,

Omodunbi

et al. 2024

dujopas

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The handiness of mercantile mechanical ventilators is highly limited in low-income countries due to high rate in the price of its affordability. This factor affects the treatment of so many patients suffering from chronic respiratory syndrome. In this research, a low- cost and easy to use pressure ventilator coupled with a remote health monitoring system to assist the doctors check up on the patients is developed. There is need to maximize the availability of the ventilators in low-income countries at an affordable price tag. This system was developed with the aid of an Arduino nano, ESP8266 based NodeMcu microcontroller, high pressure blower, pressure transducers, humidity and temperature sensor. The developed system was evaluated and compared with existing one, using an actively breathing patient simulator as it mimics through a range of respiratory diseases. In the end, we were able to design a low-cost smart health monitoring system that incorporates ventilator and health vitals monitor. Our Design was implemented and verified to be working and equally satisfying all the specified requirements.

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“…In recent times, an increasing number of researchers have been incorporating artificial intelligence into their work [3,13]. There have been discussions about whether artificial intelligence can become a worthy replacement for medical professionals [6,12]. Overall, the scientific community believes that such a replacement will not occur in the immediate future, but artificial intelligence can be beneficial for medical practitioners by assisting them in making more objective decisions or even displacing human judgment in certain aspects.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence – Application in Forensic Medicine

Kotsyubynska,

Voloshynovych,

Solodjuk

et al. 2023

Clin. and prev. med.

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Introduction. Artificial neural networks are widely utilized in medical fields, such as dentistry, molecular genetics, immunology, cardiology, and others. Forensic medicine is no exception, as artificial neural networks are also beginning to find applications in this field. The aim of this study was to demonstrate the potential for predicting human anthropometric parameters using dermatoglyphic parameters, which could enhance the method of dermatoglyphic identification. Materials and methods. We analyzed dermatoglyphs of the hands and feet from 567 individuals aged 18 to 59 years, with no genetic or endocrine disorders and no musculoskeletal problems. Results and discussion. The outcome of our work resulted in the development of the "Dermatoglyphics For Prediction (DFP)" program [Author's Certificate No. 74561. Computer program "Forensic Medical Identification Program using Artificial Neural Networks" Registration date: 07.11.2017]. This software device, after appropriate training, enables the prediction of an individual's ethnic-territorial affiliation and the presence of specific anthropometric parameters using such input data as dermatoglyphs of the hands and feet. Conclusions. The increasing needs of the Ukrainian community for the identification of unknown individuals, given the geopolitical situation related to Russian invasion in Ukraine (the constant threat of ballistic missile attacks and unmanned aerial vehicles across the entire territory of Ukraine, which could lead to mass casualties), justify the relevance and the search for innovative approaches to dermatoglyphic identification expertise, utilizing state-of-the-art technologies, particularly neural network-based prediction of anthropometric parameters, sex, and ethnic-territorial affiliation of an unknown person, using input parameters such as dermatoglyphs of the hands and feet, with the aim of enhancing the evidentiary value of identification expertise. This software device, after appropriate training, allows for the prediction of ethnic-territorial affiliation and the presence of specific anthropometric parameters in the examined individual using such input data as dermatoglyphs of the hands and feet.

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Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients

Cited by 10 publications

References 39 publications

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

Cushy and Cheap Smart Health Monitoring System Incorporating a Ventilator

Artificial Intelligence – Application in Forensic Medicine

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