Patient Monitoring Research Papers - Academia.edu (original) (raw)

2025, IET Wireless Sensor Systems

Sensor-cloud computing is envisioned to be one of the key enabling technologies for remote health monitoring. Integration of sensed data into cloud applications in sensor-cloud will help in real-time monitoring of patients over geographically distributed locations. In this study, the authors study the optimal gateway selection problem in sensor-cloud framework for real-time patient monitoring system by using a zero-sum game model. In the proposed model, a gateway acts as the first player, and chooses the strategy based on the available bandwidth, whereas a user request acts as the second player, and follows the strategy chosen by the first player. The authors evaluate the execution time for selecting the optimal gateway through which the sensed data will be fetched to the cloud platform. In addition, the authors show how user requests are serviced by the gateways to access data from cloud platform optimally. The authors also show that by using the proposed approach, the execution time decreases, thereby helping in forming a reliable, efficient and real-time architecture for health monitoring.

2025, Ambient Assisted Living

This paper presents a preliminary study to design a couple of robots, conceived to assist senior citizens 65+ in domestic and public space. The design and development of these two robots, named Domestic and Condominium, concerned, from one hand, appropriate criteria of acceptability, usability, aesthetic and safety, and on the other hand specific functionalities to satisfy users' needs.

2025, Preprint (not peer-reviewed)

In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure, distributed web system architecture that integrates realtime sensor data with a custom Customer Relationship Management (CRM) module to optimize patient monitoring and clinical decision-making. The architecture leverages IoT-enabled medical sensors to capture physiological signals, which are transmitted through secure communication channels and stored in a modular EHR system. Security mechanisms such as data encryption, role-based access control, and distributed authentication are embedded to address threats related to unauthorized access and data breaches. The CRM system enables personalized healthcare management while respecting strict privacy constraints defined by current healthcare standards. Experimental simulations validate the scalability, latency, and data protection performance of the proposed system. The results confirm the potential of combining CRM, sensor data, and distributed technologies to enhance healthcare delivery while ensuring privacy and security compliance.

2025, International Journal of Electrical and Computer Engineering (IJECE)

The internet of things (IoT) refers to a network of physical devices embedded with sensors, software, and communication tools, which allow for seamless exchange and collection of data. This technology enables automation, continuous monitoring, and data-driven decision-making across a variety of fields. In the healthcare sector, the integration of IoT with artificial intelligence (AI) is transforming how patient care is delivered, providing real-time health monitoring, personalized treatment options, and more efficient management of healthcare resources. This study investigates the significant influence of the IoT and AI on the healthcare system, focusing on how these technologies improve patient outcomes and streamline healthcare operations. It also highlights emerging challenges in the adoption of these technologies and suggests potential solutions to address these obstacles and enhance healthcare delivery. The research is based on an in-depth review of AI and IoT applications in healthcare, uncovering advancements in patient monitoring, disease management, and operational efficiency, while also identifying key challenges such as data privacy concerns and issues with system interoperability.

2025, Scientific Research Publishing, USA

IoT technology has emerged as a valuable tool in modern healthcare, providing real-time monitoring of patients, effective management of healthcare, and proper administration of patient information. The proposed system aims to develop a system that can prevent backward blood flow from stopping saline fluid, as well as monitor the temperature, heart rate, and oxygen level of patients by using multiple sensors like weight, temperature and heart rate, etc. Additionally, the proposed system can monitor the room temperature and humidity for contributing to the patient's overall comfort. In emergency situations, it includes an emergency push button for quick alert medical staff and initiates timely interventions. It is designed to support nurses and doctors in monitoring patients and providing timely interventions to prevent complications.

2025, Uva Clinical Research Lab 2025 © Uva Clinical Anaesthesia and Intensive Care ISSN 2827-7198

Malaria remains one of the most formidable parasitic diseases globally, exerting a substantial burden, particularly in tropical and subtropical regions. The clinical course of malaria varies significantly depending on the Plasmodium species, geographical factors, patient immune status, and timeliness of treatment initiation. Prognostic outcomes are influenced by the potential for relapse, recrudescence, and species-specific life cycles, with Plasmodium falciparum posing the greatest threat due to its rapid erythrocytic replication and severe complications. Cerebral malaria, severe malarial anemia, and nephrotic syndrome represent the most life-threatening complications, each with distinct pathophysiological mechanisms and clinical implications. The mortality associated with severe malaria remains high, particularly in pediatric populations and pregnant women. Effective management necessitates a multidisciplinary approach involving infectious disease specialists, pharmacists, and nurses to optimize treatment protocols and monitor for adverse outcomes. Preventive strategies, including vector control, patient education, and chemoprophylaxis, are pivotal in mitigating disease transmission and severity. Pre-travel consultations and tailored prophylaxis based on patient-specific risk factors are critical in non-endemic settings. This review consolidates current knowledge on malaria prognosis, complications, clinical management, and prevention, with an emphasis on improving patient outcomes through coordinated interprofessional care.

2025, One of the prime threats to companies is posed by insider threats because they generally hold valid access credentials to critical systems, thus invoking intricacies while detecting and responding to the threat. This project would suggest a novel solution to detect insider threats and make transa...

Considering the increasing number of malicious attempts, including malware attacks, which threaten data integrity and system reliability, strong cloud security has become a must. Rule-based antivirus systems and heuristic detection methods have been largely ineffective in identifying sophisticated and polymorphic malware. Thus, this research proposes an LSTM-GRU-based malware detection model, which aims for improved accuracy and real-time threat mitigation in the cloud environment. The proposed solution also boasts an LSTM and GRU structure to handle sequential data, identify malicious patterns, and predict false positives effectively. The proposed structure has been endowed with automatic countermeasures for perturbation threats at the runtime, thus strengthening the resilience of cloud resources. With an accuracy level at 98.99%, the model-with a precision of 98.55%, 98.28% recall and an F1-score of 97.67%-is fiercely opposed to other detection paradigms. The model is efficient in computation and hence extends to scalability for practical implementations. With the integration of intelligent threat response via advanced deep learning, the proposed work provides a flexible, yet efficient security system for cloud infrastructures against emerging malware threats. Results are a cause of optimism for deep learning in cybersecurity and pave the way for exciting new cloud security solutions.

2025, INTERNATIONAL JOURNAL OF INTERDISCIPLINARY INNOVATIVE RESEARCH & DEVELOPMENT

Phishing attacks are among the most rampant cyber threats, luring victims onto fraudulent websites. Existing rule-based or white-box statistical models and other traditional phishing detection methods are far from ideal. These methods are highly susceptible to manual feature engineering; they fail to capture sequential dependencies in web content and are known to show very high false positive rates. Most importantly, they are not scalable and cannot adapt to the new changes that keep emerging in phishing techniques, making them ineffective. In fact, to combat these challenges, the present research work proposes an advanced model for phishing website detection using a Bidirectional Long Short-Term Memory (Bi-LSTM) network engineered with an attention mechanism. The Bi-LSTM infrastructure effectively captures contextual dependencies across the website data, while the attention mechanism refines this by easily selecting significant features based on the patterns for phishing detection. Additionally, Particle Swarm Optimization (PSO) optimizes model parameters to enhance detection accuracy while minimizing computation complexity. With the experimental results available, the proposed model has achieved a practically superior performance improvement over conventional approaches of 12-15% accuracy and standard machine learning methods. The model becomes more dynamic for real-time phishing detection which augments the safety mechanisms against evolving cyber threats by deep learning and optimization techniques.

2025, Journal of Soft Computing Paradigm

Healthcare systems are increasingly challenged by the complexity of managing scalable and secure data. Addressing this, the proposed novel framework integrates elliptic curve cryptography (ECC), dynamic network slicing, blockchain sharding, and fuzzy logic to enable efficient, adaptive, and secure data analytics. The framework processes healthcare data from
IoT devices, wearable sensors, and patient records, ensuring real-time analytics, scalability, and robust security. By utilizing blockchain sharding for scalability, ECC for secure encryption, and fuzzy logic for decision-making, the architecture effectively overcomes the constraints of traditional systems. The results demonstrate significant improvements, including enhanced security (98%), reduced latency (10 ms), and higher scalability (5000 TPS). These advancements establish a reliable, decentralized foundation for predictive healthcare insights, resource optimization, and adaptive governance, setting a benchmark for modern healthcare data management systems.

2025, International journal of modern electronics and communication engineering

By using a hybrid approach combining robotic automation, Autoencoder-LSTM models and fuzzy cognitive maps (FCMs), this paper introduces an IoMT-based technology that offers the best Intelligent System for CKD prognosis. IoMT devices are utilized to capture real-time health data for continuous patient monitoring such as blood pressure and serum creatinine. Autoencoders are used to down-sample the data, while thenceforth sequence prediction is carried out by LSTM networks. FCMs are employed to stage the phases of CKD using a complex medical scenario and decision-making simulations. Robotics automation is made easy when processed real-time for better-quality management and accuracy. The method respects early CKD detection and successfully outperforms the conventional model by 98.96% accuracy. Objectives: The primary goal is to develop the CKD stage prediction accuracy utilizing IoMT and robotic automation while with Autoencoder-LSTM models are employed for identifying disease stages which in turn, assist to simulate complex medical decisions associated with the aforementioned prediction using FCMs. Methods: IoMT data being collected real-time and analysis to be done using autoencoders for dimensionality reduction and feature selection. FCMs predict disease state while LSTM models predict the trajectory of CKD. Robotic automation allow real-time data handling efficient.

2025, International Journal of Advanced Research in Information Technology and Management Science

Chronic kidney disease (CKD) is a huge threat to health globally. For better patient prognoses, early diagnosis and prediction are necessary. In order to improve CKD prediction in Internet of Medical Things (IoMT)-based robotic systems, this study presents a novel hybrid model that incorporates stochastic fuzzy system and bidirectional long short-term memory (Bi-LSTM). On the one hand, stochastic fuzzy systems manage uncertainty in medical data to improve decision-making; on the other, bi-LSTM is able to include information from data trends occurring either direction since they onset. Compared to traditional methods, for the detection of early CKD and real-time monitoring with 99% accuracy, 98% precision and 97% recall so it outperforms generic solutions. This study integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with Stochastic Fuzzy Systems to improve chronic kidney disease (CKD) prediction. Bi-LSTM effectively captures temporal patterns in medical data by processing sequences bidirectionally, while Stochastic Fuzzy Systems handle data uncertainty, enhancing decision-making robustness. Together, these methods aim to provide a more accurate and reliable CKD prediction framework, particularly suitable for real-time monitoring within IoMT-based robotic automation systems.

2025, SPIE Proceedings

Although structural health monitoring and patient monitoring may benefit from the unique advantages of optical fiber sensors (OFS) such as electromagnetic interferences (EMI) immunity, sensor small size and long term reliability, both applications are facing different realities. This paper presents, with practical examples, several OFS technologies ranging from single-point to distributed sensors used to address the health monitoring challenges in medical and in civil engineering fields. OFS for medical applications are single-point, measuring mainly vital parameters such as pressure or temperature. In the intra-aortic balloon pumping (IABP) therapy, a miniature OFS can monitor in situ aortic blood pressure to trigger catheter balloon inflation/deflation in counter-pulsation with heartbeats. Similar sensors reliably monitor the intracranial pressure (ICP) of critical care patients, even during surgical interventions or examinations under medical resonance imaging (MRI). Temperature OFS are also the ideal monitoring solution for such harsh environments. Most of OFS for structural health monitoring are distributed or have long gage length, although quasi-distributed short gage sensors are also used. Those sensors measure mainly strain/load, temperature, pressure and elongation. SOFO type deformation sensors were used to monitor and secure the Bolshoi Moskvoretskiy Bridge in Moscow. Safety of Plavinu dam built on clay and sand in Latvia was increased by monitoring bitumen joints displacement and temperature changes using SMARTape and Temperature Sensitive Cable read with DiTeSt unit. A similar solution was used for monitoring a pipeline built in an unstable area near Rimini in Italy.

2025, international Research Journal of Advanced Engineering and Science

Traditional deep learning methods for data analytics, typically collect data centrally, holding grave risks for privacy and computational inefficiencies due to the heavy data transfers. This would further expose the methods to cyber threats while being noncompliant with regulations. To help address those issues and presenting a Convolutional Neural Network (CNN)-based Federated Learning (FL), where sensitive data is kept decentralized while model training occurs cooperatively at different nodes. The introduction of Differential Privacy also further secures the model algorithms by adding noise to gradient updates to reduce the chance of executing successful data reconstruction attacks. The experiments demonstrate that FL-PPDL achieves accuracy on par with classifiers using standard centralized deep learning methods but much higher data security. The framework improves privacy preservation by 30% and reduces communication overhead by 25% compared to conventional methods without compromising model performance. Furthermore, data leakage risks are also reduced under scheme, causing an accuracy drop of less than 2% compared to non-private models. This research indicates how federated learning and differential privacy can redefine secure data analytics and shows that FL-PPDL can maintain a trade-off between accuracy and privacy and can thus be a practical solution for privacy-sensitive applications in the healthcare, finance, and smart cities domains.

2025

The paper presents am initial implementation of a patient monitoring system that may be used for patient activity recognition and emergency treatment in case a patient or an elder falls. Sensors equipped with accelerometers and microphones are attached on the body of the patients and transmit patient movement and sound data wirelessly to the monitoring unit. Applying Short Time Fourier Transform (STFT) and spectrogram analysis on sounds detection of fall incidents is possible. The classification of the sound and movement data is performed using Support Vector Machines. Evaluation results indicate the high accuracy and the effectiveness of the proposed implementation.

2025, Antimicrobial Agents and Chemotherapy

The therapeutic responses to the eight most widely used antimalarial drugs were assessed in 207 adult patients with Plasmodium vivax malaria. This parasite does not cause marked sequestration, so parasite clearance can be used as a direct measure of antimalarial activity. The activities of these drugs in descending order were artesunate, artemether, chloroquine, mefloquine, quinine, halofantrine, primaquine, and pyrimethamine-sulfadoxine (PS). Therapeutic responses to PS were poor; parasitemias did not clear in 5 of the 12 PS-treated patients, whereas all the other patients made an initial recovery. Of 166 patients monitored for ≥28 days, 35% had reappearance of vivax malaria 11 to 65 days later and 7% developed falciparum malaria 5 to 21 days after the start of treatment. There were no significant differences in the times taken for vivax malaria reappearance among the different groups except for those given mefloquine and chloroquine, in which all vivax malaria reappearances develo...

2025, The Journal of Clinical Endocrinology & Metabolism

Eleven patients who had undergone total thyroidectomy for differentiated thyroid cancer and who were on chronic TSHsuppressive therapy with levothyroxine (L-T 4 ), underwent 24-h Holter electrocardiogram and Doppler-echocardiography before and after acute recombinant human TSH (rhTSH) administration for disease staging. The treatment, which was generally well tolerated, did not affect circulating thyroid hormones levels, nor did it have measurable effects on heart rate, rhythm, left ventricular morphology, or systo-diastolic function. Notably, arterial blood pressure tended to be slightly reduced after rhTSH administration, although in no instance did the patients become frankly symptomatic. Our data demonstrate that rhTSH does not alter cardiovascular function acutely. Consequently, it can safely be used in the routine staging of patients affected by differentiated thyroid cancer. (

2025, The 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010)

Hospitals aim at an extensive continuous monitoring of patients. This enables the personal to check the conditions of a patient anywhere at any given time and allows them to immediately react to anomalies and emergencies. The same technology can be used to instantaneously visualize available patient data using augmented reality techniques.

2025

Background: Optimization of cardiac output (CO) has been evidenced to reduce postoperative complications and to expedite the recovery. Likewise, CO and other dynamic cardiac parameters can describe the systemic blood flow and tissue oxygenation state and can be useful in different clinical fields. This study aimed to validate the qCO monitor (Quantium Medical, Barcelona, Spain), a new device to estimate CO and other related parameters in a continuous, fully non-invasive way using advanced digital signal processing of impedance cardiography. Methods: The LiDCOrapidv2 (LiDCO Ltd, London, UK) was used to compare the performance of the qCO in 15 patients during major surgery under general anesthesia. Full surgeries were recorded and cardiac output obtained by both devices was compared by using correlation and Bland-Altman analysis. Results: The Bland-Altman analysis showed sufficient agreement with a mean bias of -0.03 ± 0.71 L/min. Conclusions: The findings showed that both systems off...

2025, 2001 Enterprise Networking, Applications and Services Conference Proceedings.. EntNet@SUPERCOMM2001 (Cat. No.01EX543)

Healthcare has been recognized as one of the most important areas for networked enterprise applications and services. Presently, information technologists and systems engineers all over the world are working towards achieving better efficiency and quality of service in various sectors of healthcare, such as telemedicine, hospital management, patient-care, and treatment. This paper addresses the issue of more effective and efficient handling of doctor-patient relationship. The idea is to use Software Agent technology to extend the patient-doctor relationship beyond the physical and logistical limitation of face-to-face consultations in the management of diabetes. Software agents can provide an extension of the doctor by interacting with the patient via a computer and the Internet. The agent is visualized as an anthropomorphic figure to further enhance the patient interaction.

2025, 2010 7th IEEE Consumer Communications and Networking Conference

This paper presents a Secured Wireless Sensor Network-integrated Cloud computing for u-Life Care (SC 3 ). SC 3 monitors human health, activities, and shares information among doctors, care-givers, clinics, and pharmacies in the Cloud, so that users can have better care with low cost. SC 3 incorporates various technologies with novel ideas including; sensor networks, Cloud computing security, and activities recognition. 1 In recent years, Wireless Sensor Networks (WSNs) have been employed to monitor human health and provide life care services. Existing life care systems simply monitor human health and rely on a centralized server to store and process sensed data, leading to a high cost of system maintenance, yet with limited services and low performance. For instance, Korea u-Care System for a Solitary Senior Citizen (SSC) monitors human health at home and provide limited services like 24 hours×365 days safety monitoring services for a SSC, emergency-connection services, and information sharing services. In this paper, we propose a Secured WSN-integrated Cloud computing for u-Life Care (SC 3 ) which monitors not only human health but also human activities to provide lowcost, high-quality care service to users.

2025, The 12th IEEE International Conference on e-Health Networking, Applications and Services

Ubiquitous Life Care (u-Life care) nowadays becomes more attractive to computer science researchers due to a demand on a high quality and low cost of care services at anytime and anywhere. Many works exploit sensor networks to monitor patient's health status, movements, and real-time daily life activities to provide care services to them. Context information with real-time daily life activities can help in better services, service suggestions, and change in system behavior for better healthcare. Our proposed Secured Wireless Sensor Networkintegrated Cloud Computing for ubiquitous -Life Care (SC 3 ) monitors human health as well as activities. In this paper we focus on Human Activity Recognition Engine (HARE) framework architecture, backbone of SC 3 and discussed it in detail. Camera-based and sensor-based activity recognition engines are discussed in detail along with the manipulation of recognized activities using Context-aware Activity Manipulation Engine (CAME) and Intelligent Life Style Provider (i-LiSP). Preliminary results of CAME showed robust and accurate response to medical emergencies. We have deployed five different activity recognition engines on Cloud to identify different set of activities of Alzheimer's disease patients. I.

2025, Indonesian Journal of Electrical Engineering and Computer Science

Alzheimer’s disease (AD) is a neurological disorder that results in the death of brain cells, causing memory loss, behavioral changes, and cognitive impairment. It drastically affects the individual’s work and social life, often leading to death, and is now the sixth leading cause of mortality worldwide. AD patients have limited mobility, which restricts their movement outside their homes. Thankfully, new internet of things (IoT) applications have made it possible to monitor people with various illnesses in their everyday lives, providing valuable assistance to caregivers. This study aims to create an IoT prototype that can locate an AD patient in real time and remind them to take their medication on schedule via an alarm. The small, lightweight, portable patient carrier has a NodeMCU-23DSP board, a Neo-06 global positioning system (GPS) module, and a wireless modem/Wi-Fi router. Remote patient follow-up through the Blynk 2.0 application on computers and Android devices allows for m...

2025, URF Publishers

The purpose of this article is to describe and analyze two different ocular manifestations of acute unilateral HSV infection in a 13-year-old and a 16-year-old male patient that were referred in the emergency ophthalmologic department of Pediatric General Hospital of Pentelis during February 2025 and March 2025. This report reveals the rapid clinical aggravation of the disease that requires immediate intervention with the appropriate pharmaceutical agents and formulation of a strict monitoring protocol in order to prevent visual deterioration and emergence of sight threatening complications in this vulnerable population group.

2025

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models.

2025, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Healthcare decision support systems (DSS) play a crucial role in assisting clinicians with diagnosis and treatment planning. However, the lack of interpretability in machine learning models often leads to trust issues, limiting their adoption in clinical settings. This project presents an interpretable machine learningbased decision support system using the Random Forest algorithm to enhance transparency and accuracy in healthcare predictions. The system preprocesses healthcare data, applies feature selection, and generates interpretable insights through feature importance visualization. It integrates with hospital information systems (HIS) for real-time decision-making, ensuring seamless clinical workflow incorporation. Additionally, the project explores natural language processing (NLP) techniques to analyse text-based medical data, improving contextual understanding and decision accuracy. By balancing interpretability and predictive performance, this system enables clinicians to make informed decisions while enhancing trust in AI-driven recommendations. Model validation is conducted using real-world datasets and evaluated through metrics such as accuracy, F1-score, and AUC-ROC. The proposed approach contributes to the development of reliable, interpretable, and efficient healthcare AI solutions that can significantly improve patient outcomes.

2025, The Journal of Clinical Endocrinology & Metabolism

In this study, we have investigated in vivo the time-dependent effects of TSH on vascular endothelial growth factor (VEGF) production in patients monitored for thyroid carcinoma. Serum VEGF, thyroglobulin (Tg), and TSH levels were assayed at baseline and 6, 24, 30, 48, 72, and 96 h and 1 wk after administration of recombinant human TSH (rhTSH) in 45 thyroidectomized patients affected by differentiated thyroid carcinoma. At baseline, the patients with metastasis (18 cases) showed serum Tg and VEGF values significantly higher than those seen in the cured patients (27 cases). During rhTSH stimulation, the mean VEGF levels decreased significantly in both patient groups. In 60% of patients with metastasis, VEGF nadir occurred at the same time as serum TSH reached the highest values, whereas in 85.7% of the cured patients VEGF decreased after the TSH peak (P ‫؍‬ 0.003). In conclusion, we demonstrate for the first time that shortterm administration of rhTSH in patients monitored for differentiated thyroid carcinoma induces a significant reduction in serum VEGF values even in the absence of thyroid tissue. This result would suggest that TSH may be able in vivo to regulate VEGF production from tissues other than the thyroid gland. (

2025

In (2) the phrases from (l) are represented as sequences of feet. The digit I stands for the primary stress and 2 for secondary (or tertiary) stresses (as in Kraska-Szlenk 1995 or Rubaeh and Booij 1985). Polish words have penultimate stress, i.e. a prosodie word (henceforth PW d) has a prominent trochaic foot at the right edge. 2 Following McCarthy and Prince (1993) and Selkirk (1995), I assurne that feet are binary and that some unstressed syllables remain unparsed, i.e. -10in (2a) and -szczesin (2b).

2025, 2013 IEEE International Conference on RFID (RFID)

After comparing the properties of analog backscatter and digital backscatter, we propose that a combination of the two can provide a solution for high data rate battery free wireless sensing that is superior to either approach on its own. We present a hybrid analog-digital backscatter platform that uses digital backscatter for addressability and control but switches into analog backscatter mode for high data rate transmission of sensor data. Using hybrid backscatter, we report the first digitally addressable real-time battery free wireless microphone. We develop the hybrid backscatter platform by integrating an electret microphone and RF switch with a digital RFID platform (WISP). The hybrid WISP operates by default in digital mode, transmitting and receiving digital data using the EPC Gen 2 RFID protocol but switching into analog mode to backscatter audio sensor data when activated by Gen 2 READ command. The data is recovered using a USRP-based Software Defined RFID reader. We report an operating range of 7.4 meters for the analog backscatter microphone and 2.7 meters for hybrid microphone with 26.7 dBm output power USRP-based RFID reader.

2025, 2008 Computers in Cardiology

Changes in heart rate (HR) and respiratory rate (RespR) may be used as markers of early decompensation in chronic heart failure (CHF) patients monitored at home. Aiming at improving quality of care and at reducing hospitalization rate and health care costs in CHF, progress in technology has led to the development of small portable and even wearable devices for the acquisition and transmission of relevant vital signs to a remote monitoring centre. This paper describes a signal acquisition and processing system, based on a wearable textile-based device with sensors for the measurement of one-lead ECG and chest movement, and focuses on the algorithms for HR and RespR evaluation. An electronic board collects and transmits these signals to a PDA, which sends them via Wi-Fi to a home gateway where the HR and the RespR time series are produced. The home gateway packs the data with other vital signs collected by using different devices and sends them in XML format to a central repository where a clinical decision support system can use them for the detection of early decompensation episodes. The system has successfully overcome a preliminary test phase and is ready for more extensive tests in a real clinical environment.

2025, 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2.

This paper deals with a new medical information system called Alarm Net designed for smart healthcare. Based on an advanced Wireless Sensor Network (WSN), it specifically targets assisted-living residents and others who may benefit from continuous and remote health monitoring. We present the advantages, objectives, and status of the system built at the Department of Computer Science at UVA. Early results of the prototype suggest a strong potential for WSNs to open new research perspectives for ad hoc deployment of multi-modal sensors and improved quality of medical care.

2025, Proceedings of the 35th Annual Hawaii International Conference on System Sciences

Neonatal Intensive Care Units (NICUs) require equipment and facilities to assist and monitor premature and some term babies. This equipment outputs physiological and clinical data, but current research does not provide doctors with techniques for capturing this data in a format suitable for analysis and research. Additionally, regional hospitals provide limited NICU support, but without access to a Neonatologist, the baby must be moved to another hospital. This paper details the framework for clinical and physiological data capture, the storage structures within the e-Baby Data Warehouse and information access through a secure Intranet/Internet browser. The key contribution of this work is the infrastructure that provides a platform for patient information data capture, storage, display and analysis. A key benefit of this work is to provide a mechanism for Neonatologists to receive information directly from a regional hospital, thereby preventing, in some cases, the immediate need to move the baby.

2025, Fifth International Conference on Information Technology: New Generations (itng 2008)

In this paper the authors describe the implementation and validation of a prototype of an environmental and health monitoring system based on a Wireless Sensor Network (WSN). The solution proposed for our system combines environmental and medical sensors in order to monitor both the surrounding area of the patient and the patient's health status simultaneously. This feature would allow a comprehensive understanding of the patient's condition by the specialist caring for the subject. Another key feature of the system is the development of an architecture which provides an easy, viable, cheap and effective way for connecting our environmental and medical sensor network of MicaZ motes to the outside world using Simple Network Management Protocol (SNMP) version 3. A series of experimental scenarios were developed and implemented in a laboratory setting; firstly for evaluating the reactivity of the monitoring system to changes and secondly for understanding the reliability of the data obtained for benchmarking purposes. The conclusion considers the implementation of future improvements to the health monitoring network by introducing new sensors and location tracking capabilities, and by integrating alarm triggering algorithms and advanced security techniques.

2025, International Journal of Advances in Engineering and Management

In modern healthcare cloud networks, edge AI and blockchain have emerged as a revolutionary big data analytics solution. This effective system relies on blockchain technology to protect cloud-based analytics after local data preprocessing through the use of edge computing. In healthcare applications, the proposed architecture reduces latency, enhances processing efficiency and ensures data integrity. Key components feature normalization, blockchain encryption for secure data transfer and anomaly detection using outlier elimination. Substantial improvements in transaction throughput, latency reduction and efficient data aggregation are demonstrated through performance testing. The results indicate a reduction in cloud latency over edge devices and an enhancement in blockchain transaction throughput to a maximum of 17 TPS. The efficiency of edge-cloud processing integration is also analyzed, showing seamless data distribution and minimal computational overhead. This work illustrates how AI-based edge computing and blockchain technology can be integrated to offer reliable and scalable health analytics. Federated learning models will be enhanced in the future and blockchain consensus protocols will be better optimized to provide faster real-time data processing.

2025, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

2025, Proceedings of the Custom Integrated Circuits Conference

New electronics for non-invasive medical monitoring promise low-cost, maintenance-free, and lightweight devices. These devices are critical in long-term medical measurements and in home-based tele-monitoring services, which are extremely important for the reduction of health care costs. Here, we present several methods for reducing power consumption while retaining precision. In particular, we focus on the monitoring of the heart-because of its importance-and we describe a micropower electrocardiograph, an ultra-low-power pulse oximeter, an ultra-low-power phonocardiograph, an integratedcircuit switched-capacitor model of the heart, and a low-power RF-antenna-powered CMOS rectifier for energy harvesting. We also introduce an ultra-low-power platform for medical monitoring that enables the integration of monitoring circuitry in a wireless, low-cost, and battery-free device, and describe a method for audio localization of the device in case of a medical alarm.

2025, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

In respect to the main goal of our ongoing work for predicting preterm birth, we analyze in this paper the complexity of the uterine electromyography (EMG) by using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we measure the SampEn over multiple scales using the wavelet packet decomposition method. The results obtained from the analyzed data indicate that SampEn decreases along pregnancy. Furthermore, we demonstrate that the computed SampEn parameter may discriminate between the two classes (pregnancy/labor). The results are supported by statistical analysis using t-test indicating good statistical significance with a confidence level of 95%. A surrogate data test is also performed to investigate the nature of the underlying dynamics of our experimental data. The results are very promising for monitoring pregnancy and detecting labor to help identify preterm labor.

2025, 2010 IEEE Long Island Systems, Applications and Technology Conference

With the aging of the baby boomers, it is predicted that the US population over age 65 will grow from its 1999 level of 34.6 million persons to approximately 82 million in 2050, a 137% increase. The most rapid surge in our senior population will take place between 2011 and 2030. During this 19-year interval, seniors will expand from 13% of our population to 22% of our population. In this project, our goal is to design a wireless sensor system, the Health Tracker 2000, that can monitors users' vital signs and notifies relatives and medical personnel of their location during life threatening situations. The Health Tracker 2000 combines wireless sensor networks, existing RFID (Radio Frequency Identification) and Vital Sign Monitoring technology to simultaneously monitor vital signs while keeping track of the users' location. The use of wireless technology makes it possible to install the system in all types of homes and facilities. Radio frequency waves can travel through walls and fabric, sending the vital sign and location information to a central monitoring computer via a miniature transmitter network. Such information can easily be accessed from any location over the Internet.

2025

The use of physical exercise in the treatment of myocardial ischemia is becoming increasingly important due to its beneficial effects on morbidity and mortality rates. However, there are still negative cultural aspects that limit this practice. A case study is presented of an ischemic patient monitored during an exercise program, with brief clinical comments and a report on electrocardiographic and

2025, Medical Engineering & Physics

This paper discusses the design and operational assessment of a minimum-power, 2.45 GHz portable pulse receiver and associated base transmitter comprising the interrogation link in a duplex, cross-band RF transponder designed for short-range, remote patient monitoring. A tangential receiver sensitivity of Ϫ 53 dBm was achieved using a 50 ⍀ microstrip stub-matched zero-bias diode detector and a CMOS baseband amplifier consuming 20 A from ϩ 3 V. The base transmitter generated an on-off keyed peak output of 0.5 W into 50 ⍀. Both linear and right-hand circularly-polarised antennas were employed in system evaluations carried out within an operational Coronary Care Unit ward. For transmitting antenna heights of between 0.3 and 2.2 m above floor level, transponder interrogations were 95% reliable within the 82 m 2 area of the ward, falling to an average of 46% in the surrounding rooms and corridors. Separating the polarisation modes, using the circular antenna set gave the higher overall reliability.

2025, Proceedings of the 3rd International ICST Conference on Body Area Networks

Falls are currently a leading cause of death from injury in the elderly. The usage of the conventional assistive cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S.. While canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing cane motion leads to increased risk. This paper describes the development of the SmartCane assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of assistive devices.

2025, International Symposium on Antennas and Propagation

2025, IEICE Proceeding Series

2025

The proposed system presented in this paper is able to monitor biosignals and to transmit relevant information remotely. Artificial intelligence improves the biosignals monitoring efficiency and help critical caretakers to speed up a prior diagnose. This application demonstrates how to develop new wireless intelligent system, to be future used in processing, analyzing and extracting necessary features from biosignals, easily and conveniently. The hard-soft systems for portable applications and based on client server architecture, aim biosignals monitoring in order to establish a prior diagnose.

2025, 2008 Computers in Cardiology

2025

Neurodegenerative diseases (NDD) require a constant care and attention in pharmacological therapy and rehabilitation, exercises administration, functional assessments and management of daily life. In this paper, we discuss a possible role of pervasive solutions with respect to NDD, on the basis of the overall progress of ICT and on the experience achieved in our department in three fields: Assistive Technology, NDD quantitative assessment, and design and development of wearable devices.

2025, Proceedings of the International Conference on Biomedical Electronics and Devices

As a consequence of increasing life expectancy, the promotion of lifestyles that allow aging wellbeing guarantees has acquired great importance in the developed countries. However, the adherence to healthy behaviors in young and adult people remains as a big problem in the community health field. The development of markers of adherence to healthy lifestyles and the evaluation its effectiveness is a goal of many research groups. This paper presents a system for analyzing physiological, psychological and behavioural user's habits using a smartphone and externals biodevices. We use an Android smartphone with an internal tri-axial accelerometer and GPS to monitor physical activity. The smartphone is connected via Bluetooth to a respiratory sensor for breath monitoring. In addition, Android application contains psychological questionnaires to analyze user's mood state and at the same, social interaction is analyzed tracking phone usage and user's social network. Finally, the collected information is sent to a remote server for a long-term processing.

2025, IEEE Journal of Biomedical and Health Informatics

2025, IEEE

This research offers a solution for AI in IoT-based health care systems that can help large scale implementation of AI-based cloud platforms for real-time data processing and analysis. The IoT sensors with the cloud-enabled environment supported by the CNNs and LSTM networks for health condition monitoring allows real-time management of patient health status. Based on the experimental context, the system was able to prove that the system can actually filter large data stream and work even through the fluctuating network environment. Specifically, the performance targets such as the time taken to process the data, AI output accuracy, the ability of the system to scale and the robustness of the system were assessed. The results demonstrate that the proposed solution has accuracy of up to 96% in health predictions and is easily scalable in terms of the number of IoT devices involved. Network latency was also well controlled, whereby the lowest network latency for networks was measured in 5G networks. Such conclusions prove that the application of cloud-based AI systems can enhance the conditions of the healthcare provisioning, in terms of timely identification of patients with certain health conditions.

2025, Journal of Clinical Microbiology

A group of 76 consecutive human immunodeficiency virus (HIV)-positive patients with fever of unknown origin (n ‫؍‬ 52) or fever associated with pulmonary diseases was evaluated in order to assess the usefulness of PCR with peripheral blood in the diagnosis and follow-up of visceral leishmaniasis. We identified 10 cases of visceral leishmaniasis among the 52 patients with fever of unknown origin. At the time of diagnosis, all were parasitemic by PCR with peripheral blood. During follow-up, a progressive decline in parasitemia was observed under therapy, and all patients became PCR negative after a median of 5 weeks (range, 6 to 21 weeks). However, in eight of nine patients monitored for a median period of 88 weeks (range, 33 to 110 weeks), visceral leishmaniasis relapsed, with positive results by PCR with peripheral blood reappearing 1 to 2 weeks before the clinical onset of disease. Eight Leishmania infantum and two Leishmania donovani infections were identified by PCR-restriction fragment length polymorphism analysis. PCR with peripheral blood is a reliable method for diagnosis of visceral leishmaniasis in HIV-infected patients. During follow-up, it substantially reduces the need for traditional invasive tests to assess parasitological response, while a positive PCR result is predictive of clinical relapse.