Monitoring Of Vital Parameters Research Articles

The Wireless Health Monitoring for Neonatal Care

Abstract: The wireless health monitoring for neonatal care project addresses the critical need for advanced and non-intrusive health monitoring solutions for newborns in neonatal care settings. Approximately 10% of newborns require assistance at birth, and preterm infants often require specialized stabilization or resuscitation. Traditional monitoring methods, such as electrocardiography (ECG) and pulse oximetry (PO), face challenges in electrode application, delayed HR readings, and potential inaccuracies in assessing cardiac output. This project aims to develop a wireless health monitoring system that overcomes these limitations and offers real-time and accurate monitoring of vital parameters, with a focus on heart rate. The proposed system leverages wireless technology to enable continuous and non-intrusive monitoring, enhancing the efficiency of neonatal care in delivery rooms and neonatal intensive care units (NICUs). By incorporating state-of-the-art sensors and communication protocols, the system aims to provide timely and reliable data on neonatal vital signs. The wireless nature of the solution eliminates concerns related to electrode application difficulties, equipment availability, and the risk of hypothermia.

TELEMONITORING IN PATIENTS WITH CHRONIC HEART FAILURE: EXPERIENCE FROM THE TELECUORE PROJECT IN PIACENZA

Abstract Background Starting from February 2023, the Piacenza’s Hospital has approved the use of a cardiological telemonitoring system called ‘TeleCuore’ project. This system, through the monitoring of various vital parameters which are frequently sent by patients via a dedicated platform, allows for remote assessment of the clinical status of 250 individuals with chronic heart failure (CHF). Objectives The objective of this study is to analyze the number and nature of alerts in the data sent by patients and the consequences on patient’s management derived from the alerts received through the telemedicine system. Materials and Methods 250 patients with CHF identified following recent episodes of clinical instability have been equipped with a home monitoring device (Adilife system). This involves collecting the following data: body weight, temperature, blood pressure, heart and respiratory rate, oxygen saturation, and single–lead EKG. These data are daily filtered by perfusionist technicians (TFCPC) and dedicated nurses, reviewed by the Case Manager and cardiologists. In case of recognition of altered parameters, the platform generates alerts according to pre–set clinical parameters, enabling proactive and personalized interventions on patients. In the 9 months of the TeleCuore project, 494 alerts were received, with 119 (24%) judged not clinically relevant, while 375 (76%) required operator intervention. Of these, 99 were due to weight gain (26%), 40 to low blood pressure (11%), 42 to high blood pressure (11%), 33 to tachycardia episodes (9%), 30 to bradycardia (8%), 49 for technic issues with the weight scale (13%), 78 for EKG artifacts (21%), and 4 for previously unknown atrial fibrillation (1%). 25 patients (8%) were invited for ambulatory clinical evaluation, 28 (7%) were managed through teleconsultation, and the remaining majority (85%) was handled via telephone contact for therapy adjustments (60.5%). 39.4% underwent re–training on the correct device use in the first month. Conclusions The TeleCuore Project is a pilot cardiological telemedicine initiative. Initial data confirm the utility of this system in optimizing the follow–up of patients with CHF, allowing for instant therapeutic adjustments based on the patient’s clinical progress. Teamwork of different healthcare professionals have emerged as crucial for the proper management of alerts. Further data will be needed to assess whether TeleCuore also helps reduce hospitalizations related to CHF.

CRP/Albumin Ratio and NLR in Recognizing Critically Ill Patients

Managing critically ill patients in the emergency department requires a comprehensive approach that encompasses rapid recognition, timely intervention, and continuous monitoring. These patients present with a spectrum of conditions ranging from trauma to septic shock, necessitating immediate medical attention to prevent further deterioration. The initial assessment follows the ABCDE approach, focusing on Airway, Breathing, Circulation, Disability, and Exposure. Resuscitation and stabilization efforts aim to restore perfusion and oxygenation to vital organs through fluid resuscitation, vasopressor therapy, and mechanical ventilation. Ongoing care involves vigilant monitoring of vital signs, laboratory parameters, and organ function, guiding therapeutic interventions, and informing decisions regarding escalation or de-escalation of care. Laboratory parameters, including inflammatory markers like C-reactive protein and neutrophil-to-lymphocyte ratio, play a crucial role in assessing the patient's condition, monitoring response to treatment, and predicting clinical outcomes. Multidisciplinary collaboration ensures comprehensive care tailored to the patient's needs, ultimately optimizing outcomes and reducing mortality in this high-risk population.

Aquaculture Monitoring and Control System Based On IOT

- This project offers a comprehensive solution for monitoring and regulating aquaculture conditions using cutting-edge Internet of Things (IoT) technology. By leveraging ESP8266 NodeMCU and Arduino UNO, alongside a range of sensors including the DS18B20 waterproof temperature sensor, water level sensor, and turbidity sensor, it ensures real-time monitoring of vital parameters in aquatic environments. Precise control of water circulation and management is made possible through the integration of motor drivers with submersible pumps. The Blynk App serves as the user-friendly control interface, granting remote access and management of water pumps. Meanwhile, sensor data is continuously collected and displayed on ThingSpeak, offering consumers valuable insights into the current state of their aquaculture setup. This project harnesses IoT technology to enhance aquaculture management efficiency and productivity, while also ensuring optimal environmental conditions for aquatic life. Its modular design enables scalability and customization, catering to various aquaculture configurations and settings. Key Words: Arduino UNO, Sensors, Motor Driver, Wifi Module, ThingSpeak, IOT, Aquaculture, Blynk App.

Voice Recognition Based Vital Parameter Monitoring Patient BED

In healthcare protocols continuous monitoring of vital parameters plays a crucial role in patient care. Integrating advanced technologies for both monitoring and patient comfort enhances the efficiency of healthcare delivery. This paper presents the design and development of a novel patient bed system equipped with vital parameter monitoring capabilities and adjustable head position controlled through voice commands. The proposed system leverages state-of-the-art sensors to monitor vital parameters such as heart rate, blood pressure, oxygen saturation, and temperature. These sensors are seamlessly integrated into the bed, providing non-intrusive and continuous monitoring of the patient's health status. Real-time data acquisition and processing algorithms ensure accurate and timely assessment of the patient's condition. Furthermore, the patient bed is designed with an adjustable head position feature, allowing for optimal comfort and support. Unlike conventional manual or automated adjustment mechanisms, our system employs voice control technology for intuitive and hands-free operation. Patients can simply issue verbal commands to adjust the head position according to their preferences, enhancing overall comfort and facilitating better rest.

ADSBAN: Anomaly detection system for body area networks utilizing IoT and machine learning

SummaryBody area networks (BANs) play a pivotal role in modern healthcare, enabling real‐time data collection and monitoring of vital patient parameters, thereby empowering healthcare providers to respond swiftly to any serious health conditions. These networks depend on strategically placed sensors on the patient's body to collect important health data. The integrity of this data is really important, as problems with a component can cause doctors to make mistakes that could be life‐threatening. Consequently, developing and evaluating robust anomaly detection methods for BANs is paramount. To address this concern, an anomaly detection system for body area networks (ADSBAN) has been proposed in this article. Different machine learning methods have been tested and implemented to improve the proposed system's performance. These include decision tree, K‐nearest neighbor, logistic regression (LR), random forest, AdaBoost (Adaptive Boosting), and XGBoost (Extreme Gradient Boosting). Further, this framework has been tested with emulated and standard datasets and compared with existing methodologies. The research employs an IoT Flock emulator to replicate BAN conditions and simulate two distinct attack scenarios. Wireshark aids in thoroughly analyzing network traffic, while Python, in conjunction with tools like Keras and Pandas, is instrumental in implementing the ML models. Further, a CIC flowmeter has been utilized to convert the .pcap file format into .CSV file format. Comprehensive evaluation metrics such as precision, recall, accuracy, F1 score, and the Mathew correlation coefficient (MCC) demonstrate the consistent superiority of LR on emulated (i.e., traffic generated through a simulator IoT Flock) and standard datasets (BoT‐IoT). LR shows the highest accuracy of 99.92% on emulated and standard datasets, while XGBoost has the highest average accuracy of 99.92% on standard dataset. This research significantly bolsters the reliability of healthcare data, instilling confidence among healthcare professionals in their decision‐making processes. By safeguarding the integrity of vital health data in BANs, it advances the quality of patient care. It underscores the indispensable role of ML techniques in fortifying the resilience of healthcare systems.

Security Drone for Surveillance in Military

Abstract: This paper presents the design and development of a sophisticated military security drone equipped with advanced components including Pixhawk flight controller, GPS module, camera, receiver, telemetry system, BLDC (Brushless Direct Current) motors, and electronic speed controller (ESC). The Pixhawk flight controller serves as the brain of the drone, managing flight dynamics, navigation, and mission planning with remarkable precision and reliability. Integrated with a highaccuracy GPS module, the drone achieves autonomous navigation capabilities, enabling it to execute pre-defined flight paths and perform missions with minimal human intervention. A high-resolution camera system is incorporated into the drone, providing real-time video surveillance and reconnaissance capabilities. This camera system is complemented by a receiver module, facilitating remote control and data transmission between the drone and ground control station.Furthermore, the telemetry system enables remote monitoring of the drone's vital parameters, including altitude, speed, battery status, and sensor readings. This real-time telemetry data enhances situational awareness and facilitates informed decision-making during missions.The propulsion system of the drone consists of BLDC motors and electronic speed controllers, offering efficient and reliable propulsion for sustained flight operations. These components are carefully integrated to optimize power consumption, flight endurance, and maneuverability, ensuring the drone's effectiveness in various operational scenarios. In summary, the integration of Pixhawk flight controller, GPS module, camera, receiver, telemetry, BLDC motors, and electronic speed controller in the design of the military security drone represents a significant advancement in unmanned aerial technology. The resulting drone platform offers enhanced capabilities for surveillance, reconnaissance, and security applications, contributing to the effectiveness and efficiency of military operations in diverse environments

IoT Based Health Monitoring System

Abstract: The rapid advancements in the Internet of Things (IoT) have paved the way for revolutionary healthcare solutions. This paper proposes an IoT-based health monitoring system aimed at providing continuous and remote monitoring of patients' vital signs and physiological parameters. This real-time data collection and analysis empowers proactive healthcare, enhancing patient safety and quality of life.

Principles of the pharmacology of local anesthetics

The development of local anesthetics revolutionized the performance of painful interventions. Local anesthetics have an effect on voltage-gated sodium channels in nerve fibers and modulate the conduction of impulses. With respect to the chemical structure, local anesthetics can be divided into amide and ester types. The structural differences of local anesthetics have an influence on the duration of action, the degradation pathways and specific side effects. Severe adverse events include cardiotoxicity and neurotoxicity. In addition to basic measures, such as the monitoring and securing of vital parameters, lipid infusion represents a treatment option in cases of intoxication. The recent developments of local anesthetics are particularly concerned with the reduction of toxicity and prolonging the duration of action.

Survival probability function as a supporting element in automated triage system on the battlefield

Mass events are incidents involving a large number of casualties. They are an integral part of combat operations. These events are characterized by exceeding the capabilities of the rescuers present at the scene during a specific phase of the operation. The difference in triage applied in combat compared to triage used in civilian mass events is directly related to their specific nature and intended objectives. The article presents an innovative segregation algorithm that takes into account the value of the so-called "survival function," which is a component of a medical evacuation decision support system based on the integration of monitoring and analysis of soldier's vital parameters with the medical security system.

Dexmedetomidine at Home for Intractable Dystonia and Insomnia in Children With Special Needs: A Case Series.

We know that syndromic conditions and severe chronic diseases can be associated with symptoms that may interfere with sleep, significantly impacting the life quality of children and caregivers. Drugs commonly used in treating insomnia, such as melatonin, benzodiazepines, niaprazine, and antihistamines, are often either ineffective or associated with adverse effects, requiring new therapeutic perspectives. Dexmedetomidine is a selective alpha-2 agonist with hypnotic and anxiolytic effects, which, by stimulating alpha-2 adrenergic receptors in the locus coeruleus, induces sleep comparable to stages 2-3 of the non-REM phase without substantially affecting the respiratory drive during sedation. Its use has already been extensively described in pediatric intensive care or procedural sedation literature. In 2018, the Italian Medicines Agency (Agenzia Italiana Del Farmaco AIFA) authorized the off-label use of dexmedetomidine outside of intensive care in Children undergoing palliative treatment to control distressing symptoms related to pathology and refractory sleep disorders, and the literature reported cases of children who received dexmedetomidine at home. Our study aims to describe the home use of dexmedetomidine in children with insomnia or intractable dystonic states. We conducted a retrospective analysis through a questionnaire addressed to 12 Italian pediatric palliative care centers regarding the home use of dexmedetomidine in sleep disorders and intractable dystonic states. We collected a case series of 9 children treated with dexmedetomidine at home, 8 via intranasal and 1 via intravenous route. All children received the first drug administration in the hospital or hospice during a dedicated admission, under close monitoring of vital signs parameters for 72 hours (3 days, range 2-7 days). After discharge, the potential side effects of the drug were explained to the patient's families, and, once informed consent was obtained, the home administration of dexmedetomidine continued, with follow-up by the palliative care team. At home, dexmedetomidine was administered for 3000 days (minimum 1 month, maximum 36 months). The first patient was treated for 1095 days, from 2019 to 2021 (discontinued due to underlying condition-related death). All patients observed a persistent benefit from the treatment on symptoms, and none of them discontinued dexmedetomidine administration due to drug-related adverse effects or perceived lack of therapeutic efficacy. Therefore, its use at home may represent a promising therapeutic approach for intractable sleep disorders or dystonic states in pediatric palliative care children. Further studies are needed to confirm our results.

Wearable Devices: Implications for Precision Medicine and the Future of Health Care.

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.

Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study

Objective. Less invasive surfactant administration (LISA) has been introduced to preterm infants with respiratory distress syndrome on continuous positive airway pressure (CPAP) support in order to avoid intubation and mechanical ventilation. However, after this LISA procedure, a significant part of infants fails CPAP treatment (CPAP-F) and requires intubation in the first 72 h of life, which is associated with worse complication free survival chances. The aim of this study was to predict CPAP-F after LISA, based on machine learning (ML) analysis of high resolution vital parameter monitoring data surrounding the LISA procedure. Approach. Patients with a gestational age (GA) <32 weeks receiving LISA were included. Vital parameter data was obtained from a data warehouse. Physiological features (HR, RR, peripheral oxygen saturation (SpO2) and body temperature) were calculated in eight 0.5 h windows throughout a period 1.5 h before to 2.5 h after LISA. First, physiological data was analyzed to investigate differences between the CPAP-F and CPAP-Success (CPAP-S) groups. Next, the performance of two types of ML models (logistic regression: LR, support vector machine: SVM) for the prediction of CPAP-F were evaluated. Main results. Of 51 included patients, 18 (35%) had CPAP-F. Univariate analysis showed lower SpO2, temperature and heart rate variability (HRV) before and after the LISA procedure. The best performing ML model showed an area under the curve of 0.90 and 0.93 for LR and SVM respectively in the 0.5 h window directly after LISA, with GA, HRV, respiration rate and SpO2 as most important features. Excluding GA decreased performance in both models. Significance. In this pilot study we were able to predict CPAP-F with a ML model of patient monitor signals, with best performance in the first 0.5 h after LISA. Using ML to predict CPAP-F based on vital signals gains insight in (possibly modifiable) factors that are associated with LISA failure and can help to guide personalized clinical decisions in early respiratory management.

Principles of the pharmacology of local anesthetics

The development of local anesthetics revolutionized the performance of painful interventions. Local anesthetics have an effect on voltage-gated sodium channels in nerve fibers and modulate the conduction of impulses. With respect to the chemical structure, local anesthetics can be divided into amide and ester types. The structural differences of local anesthetics have an influence on the duration of action, the degradation pathways and specific side effects. Severe adverse events include cardiotoxicity and neurotoxicity. In addition to basic measures, such as the monitoring and securing of vital parameters, lipid infusion represents a treatment option in cases of intoxication. The recent developments of local anesthetics are particularly concerned with the reduction of toxicity and prolonging the duration of action.

Continuous Remote Monitoring in Moderate and Severe COVID-19 Patients.

Background COVID-19 steadily built up the pressure on healthcare systems worldwide, creating the need for novel methods to alleviate the burden. Continuous remote monitoring of vital parameters reduces morbidity and mortality in hospitals by providing real-time disease data that can be analyzed through web portals. It enables healthcare workers to identify which patients require prompt administration of healthcare. Patients remain under the purview of their doctors and can be notified early if there are any deteriorations in the parameters being monitored. Aims To evaluate the use of remote monitoring in moderate and severe COVID-19 patients and to correlate the Dozee Early Warning Score (DEWS) with severity and outcome in moderate and severe COVID-19 patients. Materials and methods We conducted a prospective study on adult (>18 years old) moderate and severe COVID-19 patients during the second wave of COVID-19. The vitals of the subjects were continuously monitored using Dozee, a contactless remote patient monitoring system enabled with DEWS that reflects the overall patient condition based on respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO2). We assessed the correlation of DEWS with patients' clinical outcomes: deteriorated or recovered. Results Thirty-nine COVID-19 patients were recruited for the study, of whom 29 were discharged after recovery and 10 deteriorated and died. Respiratory rate trend, respiratory rate DEWS, SpO2 DEWS, and total DEWS showed a significant reduction in recovered patients, while the same parameters showed a significant increase followed by consistently high scores in patients who deteriorated and died due to the disease. Total DEWS was proportional to the risk of mortality in a patient. Conclusion We concluded that continuous vitals monitoring and the resulting DEWS in moderate and severe COVID-19 patients were indicators of their improvement or deterioration. DEWS uses continuous remote monitoring of routinely collected vitals (HR, RR, and SpO2) to serve as a predictor of patient outcome.

Failure analysis on outer tube breaks of a double-sleeve rapid cooling heat exchanger

Failure analysis of a double-sleeve rapid cooling heat exchanger used for ethylene cracking process is presented in this study. The outer tubes of the heat exchanger break four times in succession. The inner tube and the outer tube materials of the heat exchanger are 13CrMo4-5 and SA106Gr.B, respectively. Based on the analysis of fracture appearance, chemical composition, mechanical properties, oxidation products and metallographic microstructure, it is found that short-time local overheating resulted in the four outer tube breaks. The observed pearlite spheroidization of SA106Gr.B indicate the temperature at the break positions exceed the phase transition critical temperature AC1（735℃）. Based on the analysis of the heat transfer process and boiler water circulation, it is found that the heat transfer deterioration of in a single set of double-sleeve, i.e., water is completely converted into superheated saturated steam, is the root cause of short-time local overheating. In view of the above failure mechanism, several countermeasures were proposed including the verification of the circulation ratio to ensure that the heat absorption per unit volume of cooling water is within the threshold value should be conducted in the design process, considering the uneven distribution of cooling water and cracking gas at the inlet and the influence of shock vibration caused by water and cracked gas, an appropriate safety factor should be considered for the design of gap / water volume during operation, as well as promoting the monitoring of vital parameters (e.g., the temperature during operation).

Self-care management and experiences of using telemonitoring as support when living with hypertension or heart failure: A descriptive qualitative study

BackgroundThe need for support in self-care at home will increase with the growing older population with chronic illness. Many people have one or more chronic illnesses and struggle with self-care activities, often supported by informal carers at home. The rapid development of telemonitoring applications in primary care calls for increased knowledge about how people with chronic illness and their informal carers experience the use of telemonitoring applications at home. ObjectiveThis study aims to describe experiences of self-care management at home when living with hypertension or heart failure, with support from primary care through telemonitoring. DesignA descriptive qualitative approach was applied using semi-structured interviews with patients and informal carers in a pilot project on telemonitoring of chronic illness in primary care from October 2019 to June 2021. SettingParticipants were recruited from three primary care settings and one medical department at one hospital in a region in southern Sweden. ParticipantsA purposive sample of patients (n = 20) with chronic illness living at home and their informal carers (n = 4) were recruited. MethodsSemi-structured telephone interviews were conducted, guided by open-ended questions targeting patients’ and informal carers’ experiences of self-care management at home and using telemonitoring applications as support. Transcribed interviews were analyzed through qualitative content analysis. Results‘Developing the capability to perform self-care with technology as both an intruder and an invited guest’ was the unifying theme that tied together the experiences of patients with chronic illness and their informal carers. Experiences of self-care management included acquiring necessary self-care skills, expertise in managing their chronic illness, and reciprocal relationships with healthcare professionals when using telemonitoring application as support in self-care monitoring of vital parameters. However, uncertainty regarding the interpretation of symptoms and a feeling of exclusion were seen. ConclusionsTelemonitoring applications offer potential support for patients with chronic illnesses and their informal carers, enabling them to establish new routines and enhance motivation for self-care activities at home. This study emphasizes the adaptability of telemonitoring applications in meeting the unique support requirements of patients and informal carers when managing self-care at home.

Early Hospital Discharge Using Remote Monitoring for Patients Hospitalized for COVID-19, Regardless of Need for Home Oxygen Therapy: A Descriptive Study.

Since beds are unavailable, we prospectively investigated whether early hospital discharge will be safe and useful in patients hospitalized for COVID-19, regardless of their need for home oxygen therapy. Extending the initial inclusion criteria, 62 patients were included and 51 benefited from home telemonitoring, mainly assessing clinical parameters (blood pressure, heart rate, respiratory rate, dyspnea, temperature) and peripheral saturation (SpO2) at follow-up. 47% of the patients were older than 65 years; 63% needed home oxygen therapy and/or presented with more than one comorbidity. At home, the mean time to dyspnea and tachypnea resolutions ranged from 21 to 24 days. The mean oxygen-weaning duration was 13.3 ± 10.4 days, and the mean SpO2 was 95.7 ± 1.6%. The nurses and/or doctors managed 1238 alerts. Two re-hospitalizations were required, related to transient chest pain or pulmonary embolism, but no death occurred. Patient satisfaction was good, and 743 potential days of hospitalization were saved for other patients. The remote monitoring of vital parameters and symptoms is safe, allowing for early hospital discharge in patients hospitalized for COVID-19, whether or not home oxygen therapy was required. Oxygen tapering outside the hospital allowed for a greater reduction in hospital stay. Randomized controlled trials are necessary to confirm this beneficial effect.

Development IoT-based Infant Monitoring System for Preventing Sudden Infant Death Syndrome (SIDS) with Abnormal Condition Notifications and Lost Data Analysis

Sudden Infant Death Syndrome (SIDS) is a devastating phenomenon where an otherwise healthy baby under the age of one inexplicably passes away without any apparent symptoms. It can be triggered by a variety of factors, including the baby's sleeping position and the infant's physiological state, which may not have reached a certain stage of development. Efficient monitoring of vital parameters such as body temperature and heart rate is crucial for assessing and safeguarding the baby's health. Additionally, such monitoring can help prevent febrile seizures that may lead to epilepsy. To address this critical need, a real-time monitoring tool capable of continuously tracking body temperature and heart rate with precision is required. This research aims to develop a novel system for real-time monitoring of vital parameters, specifically Respiration Rate, utilizing Piezo Electric sensors, and heart rate monitoring using the MAX30100 sensor. The proposed method involves designing a respiration and heart rate monitoring device that integrates piezoelectric sensors and ESP32 for data processing and communication. By employing these sensors and technology, we can accurately measure and track a baby's respiration rate and heart rate, facilitating early detection of potential abnormalities. The experimental results demonstrate the effectiveness of the monitoring tool, with a maximum difference value of 28.74% when compared to existing reference tools and a minimum difference of 0.00%. This highlights the reliability and accuracy of the proposed system. Furthermore, the study offers recommendations on optimal sensor placement to enhance the tool's efficacy, ensuring precise and consistent data acquisition. The proposed monitoring system goes beyond traditional methods by providing remote access to the gathered data. Doctors and healthcare professionals can access the output results through a dedicated website, enabling seamless collaboration and timely medical interventions when required. Moreover, the system incorporates telegram notifications to alert healthcare providers in case of abnormal conditions, further reducing the risk of SIDS and associated complications. In conclusion, this research presents a valuable contribution towards preventing Sudden Infant Death Syndrome (SIDS) by developing an efficient, real-time monitoring tool for assessing vital parameters in infants. By integrating Piezo Electric sensors and the MAX30100 sensor with ESP32, the system offers accurate measurements and seamless data accessibility to healthcare professionals. The potential of this tool to save lives and protect infants from the risks of SIDS makes it a critical advancement in infant healthcare. Further improvements and wider adoption of this technology can significantly enhance infant mortality rates globally

How to leverage intelligent agents and complex event processing to improve patient monitoring

Abstract This paper describes an intelligent ecosystem that can continuously monitor patients’ health conditions, whether at home, at work or during recreational activities, by leveraging a creative blend of wearable medical devices, intelligent agents (IA) and complex event processing (CEP). With the help of a smart application, linking wearable devices and the power of IA and CEP, patients will be constantly and actively supervised during their daily activities. This can even save their lives in case they experience sudden or gradual problems. Thanks to our system, patients with chronic illnesses that are not serious but potentially unstable will no longer overburden first aid services. This is also helpful in containing the spread of COVID-19. Specifically, in this paper, we focus on automatic monitoring of vital parameters, electrocardiogram analysis and psoriasis detection. Experimental results conducted on real patients show how promising our approach is.