A Digital Twin Framework for Real-Time Healthcare Monitoring: Leveraging AI and Secure Systems for Enhanced Patient Outcomes DOI Creative Commons
Ahmed K. Jameil, H. S. Al‐Raweshidy

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

Abstract Digital Twin (DT) technology in healthcare is relatively new and faces several challenges, e.g., real-time data processing, secure system integration, robust cybersecurity. Despite the growing demand for monitoring frameworks, further improvements remain possible. In this study, an architecture has been introduced that utilises cloud computing to create a DT ecosystem. A group of 20 participants monitored continuously using high-speed track key physiological parameters, i.e., diabetes risk factors, heart rate (HR), oxygen saturation (SpO2) levels, body temperature (BT). The model functions as tool, storing both sensor historical records, effectively identify health risks anomalies. An MLP was combined with XGBoost, resulting 25% reduction training time 33% testing time. demonstrated reliability accuracy 98.9% achieved 95.4%, alongside F1 score 0.984. Meticulous attention paid cybersecurity measures, ensuring integrity through end-to-end encryption compliance regulations. incorporation AI within sector seen having potential overcome existing limitations systems, while workloads are relieved data-driven diagnostics decision-making processes improved, enhanced patient predictive analysis

Язык: Английский

Integrating the Internet of Things (IoT) in SPA Medicine: Innovations and Challenges in Digital Wellness DOI Creative Commons
Mario Casillo, Liliana Cecere, Francesco Colace

и другие.

Computers, Год журнала: 2024, Номер 13(3), С. 67 - 67

Опубликована: Март 6, 2024

Integrating modern and innovative technologies such as the Internet of Things (IoT) Machine Learning (ML) presents new opportunities in healthcare, especially medical spa therapies. Once considered palliative, these therapies conducted using mineral/thermal water are now recognized a targeted specific therapeutic modality. The peculiarity treatments lies their simplicity administration, which allows for prolonged treatments, often lasting weeks, with progressive controlled effects. Thanks to technologies, it will be possible continuously monitor patient, both on-site remotely, increasing effectiveness treatment. In this context, wearable devices, smartwatches, facilitate non-invasive monitoring vital signs by collecting precise data on several key parameters, heart rate or blood oxygenation level, providing perspective detailed treatment progress. constant acquisition thanks IoT, combined advanced analytics ML collection analysis, allowing real-time personalized adaptation. This article introduces an IoT-based framework integrated techniques tailored customer management more effective results. A preliminary experimentation phase was designed implemented evaluate system’s performance through evaluation questionnaires. Encouraging results have shown that approach can enhance highlight value significant contribution healthcare.

Язык: Английский

Процитировано

21
A digital twin framework for real-time healthcare monitoring: leveraging AI and secure systems for enhanced patient outcomes DOI Creative Commons
Ahmed K. Jameil, H. S. Al‐Raweshidy

Discover Internet of Things, Год журнала: 2025, Номер 5(1)

Опубликована: Апрель 9, 2025

Язык: Английский

Процитировано

2
The Next Generation of Health Monitoring DOI
Wasswa Shafik

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 69 - 106

Опубликована: Март 28, 2025

Digital twins and medical wearables are revolutionizing healthcare by enabling personalized, real-time monitoring predictive insights. twins, virtual replicas of patients, integrate data from to simulate health conditions, predict outcomes, optimize treatments. Medical such as smartwatches, biosensors, fitness trackers collect continuous data, providing insights into vital signs, activity levels, chronic disease management. Together, they enhance remote patient monitoring, support AI-driven diagnostics, facilitate early detection anomalies. This synergy accelerates precision medicine, improves empowers proactive healthcare, marking a transformative leap in innovation.

Язык: Английский

Процитировано

0
Hybrid deep learning for IoT-based health monitoring with physiological event extraction DOI Creative Commons
Sivanagaraju Vallabhuni, Kumar Debasis

Digital Health, Год журнала: 2025, Номер 11

Опубликована: Май 1, 2025

Objective Integrating IoT technologies into the healthcare system has significantly raised prospects for patient monitoring and disease prediction. However, present-day models have failed to effectively encompass spatial-temporal data samples. Methods This paper presents a novel hybrid machine-learning model by amalgamating Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTMs) boost prediction accuracy. Whereas CNNs extract spatial features from medical images, LSTMs temporal patterns of wearable sensor data. Such configuration increases accuracy 10% more than that achieved individual models. For better feature extraction, proposed method implements Physiological Event Extraction (PEE), which is aimed at identifying important physiological events such as heart rate variability respiratory changes raw Results helps render interpretable, providing another 15% improvement in performance. Anomaly detection employed ensemble techniques combined Isolation Forest One-Class SVM, reducing false positives 20%, thus outperforming conventional approaches. It further enhanced True Positive Rate (TPR) 25% through using an online learning algorithm Incremental Gradient Descent Momentums. Robust statistical methods based on M-estimator theory had been integrated treatment outliers missing data, helped bias estimation 30% increasing False (FPR) 12%. Conclusion All these enhancements constitute major step towards improving processing chain, thereby trusted accurate real-time health anomaly detection. In this regard, research also paves way designing next-gen analytics their actual clinical applications.

Язык: Английский

Процитировано

0
An Intelligent Framework for Real-Time Heart Stroke Monitoring and Early Detection Using Machine Learning DOI
Navneet Kumar Rajpoot, Prabhdeep Singh, Bhaskar Pant

и другие.

Cureus Journal of Computer Science., Год журнала: 2025, Номер unknown

Опубликована: Май 27, 2025

Язык: Английский

Процитировано

0
Digital Twin and sustainability: A data-driven scientometric exploration DOI
Munish Bhatia,

Rohit Kumar

Internet of Things, Год журнала: 2025, Номер unknown, С. 101652 - 101652

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy Commitment DOI Open Access

Stanley Mlato,

Yesaya Gabriel,

Prince Chirwa

и другие.

International journal of Computer Networks & Communications, Год журнала: 2024, Номер 16(2), С. 87 - 106

Опубликована: Март 29, 2024

The integration of artificial intelligence technology with a scalable Internet Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. multi-server environment within IoT introduces flexible security service model, enabling interact server through single registration. To ensure secure and privacy preservation for resources, an authentication scheme is essential. Zhao et al. recently introduced user the environment, utilizing passwords cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on al.'s scheme, focusing denial attacks, revealing lack user-friendliness. Subsequently, we propose new fuzzy commitment over addressing shortcomings scheme. Formal verification proposed conducted using ProVerif simulation tool. Through both formal informal analyses, demonstrate that resilient various known attacks those identified in

Язык: Английский

Процитировано

1
Internet of things-based secure architecture to automate industry DOI
Abdullah Aljumah, Tariq Ahamed Ahanger, Imdad Ullah

и другие.

Cluster Computing, Год журнала: 2024, Номер 27(8), С. 11103 - 11118

Опубликована: Май 16, 2024

Язык: Английский

Процитировано

1
Stochastic Game Network-inspired intelligent framework for quality assessment in logistic industry DOI
Abdullah Aljumah, Tariq Ahamed Ahanger, Imdad Ullah

и другие.

Internet of Things, Год журнала: 2024, Номер 26, С. 101205 - 101205

Опубликована: Апрель 30, 2024

Язык: Английский

Процитировано

1
Smart monitoring solution for dengue infection control: A digital twin-inspired approach DOI
Ankush Manocha, Munish Bhatia, Gulshan Kumar

и другие.

Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 257, С. 108459 - 108459

Опубликована: Окт. 10, 2024

Язык: Английский

Процитировано

1