IoT Based Nurse Activities Monitoring and Controlling System

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.

Parsimonious Model for Blood Glucose Level Monitoring in Type 2 Diabetes patients

To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were randomized to receive rapid-acting or sustained-release gliclazide therapy for 12 weeks.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

Human Respiration Rate Estimation Using Ultra-wideband Distributed Cognitive Radar System

It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare,rescue,and security applications.In this paper,we first present a multi-ray propagation model for UWB signal,which is traveling through the human thorax and is reflected on the air/dry-skin/fat/muscle interfaces,A geometry-based statistical channel model is then developed for simulating the reception of UWB signals in the indoor propagation environment.This model enables replication of time-varying multipath profiles due to the displacement of a human chest.Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate.The analytical framework can serve as a basis in the planning and evaluation of future rheasurement programs.We also provide a case study on how the antenna beamwidth affects the estimation of respiration rate based on the proposed propagation models and system architecture.

Emerging wearable technology applications in gastroenterology:A review of the literature

The field of gastroenterology has recently seen a surge in wearable technology to monitor physical activity,sleep quality,pain,and even gut activity.The past decade has seen the emergence of wearable devices including Fitbit,Apple Watch,AbStats,and ingestible sensors.In this review,we discuss current and future devices designed to measure sweat biomarkers,steps taken,sleep efficiency,gastric electrical activity,stomach pH,and intestinal contents.We also summarize several clinical studies to better understand wearable devices so that we may assess their potential benefit in improving healthcare while also weighing the challenges that must be addressed.

Machine Learning Applied to Problem-Solving in Medical Applications

Physical health plays an important role in overall well-being of the human beings.It is the most observed dimension of health among others such as social,intellectual,emotional,spiritual and environmental dimensions.Due to exponential increase in the development of wireless communication techniques,Internet of Things(IoT)has effectively penetrated different aspects of human lives.Healthcare is one of the dynamic domains with ever-growing demands which can be met by IoT applications.IoT can be leveraged through several health service offerings such as remote health and monitoring services,aided living,personalized treatment,and so on.In this scenario,Deep Learning(DL)models are employed in proficient disease diagnosis.The current research work presents a new IoT-based physical health monitoring and management method using optimal Stacked Sparse Denoising Autoencoder(SSDA)technique i.e.,OSSDA.The proposed model utilizes a set of IoT devices to collect the data from patients.Imbalanced class problem poses serious challenges during disease diagnosis process.So,the OSSDA model includes Synthetic Minority Over-Sampling Technique(SMOTE)to generate artificial minority class instances to balance the class distribution.Further,the hyperparameter settings of the OSSDA model exhibit heavy influence upon the classification performance of SSDA technique.The number of hidden layers,sparsity,and noise count are determined by Sailfish Optimizer(SFO).In order to validate the effectiveness and performance of the proposed OSSDA technique,a set of experiments was conducted on diabetes and heart disease datasets.The simulation results portrayed a proficient diagnostic outcome from OSSDA technique over other methods.The proposed method achieved the highest accuracy values i.e.,0.9604 and 0.9548 on the applied heart disease and diabetes datasets respectively.

Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics

AIM To detect blood withdrawal for patients with arterial blood pressure monitoring to increase patient safety and provide better sample dating.METHODS Blood pressure information obtained from a patient monitor was fed as a real-time data stream to an experimental medical framework. This framework was connected to an analytical application which observes changes in systolic, diastolic and mean pressure to determine anomalies in the continuous data stream. Detection was based on an increased mean blood pressure caused by the closing of the withdrawal three-way tap and an absence of systolic and diastolic measurements during this manipulation. For evaluation of the proposed algorithm, measured data from animal studies in healthy pigs were used.RESULTS Using this novel approach for processing real-time measurement data of arterial pressure monitoring, the exact time of blood withdrawal could be successfully detected retrospectively and in real-time. The algorithm was able to detect 422 of 434(97%) blood withdrawals for blood gas analysis in the retrospective analysis of 7 study trials. Additionally, 64 sampling events for other procedures like laboratory and activated clotting time analyses were detected. The proposed algorithm achieved a sensitivity of 0.97, a precision of 0.96 and an F1 score of 0.97.CONCLUSION Arterial blood pressure monitoring data can be used toperform an accurate identification of individual blood samplings in order to reduce sample mix-ups and thereby increase patient safety.

A survey on the adoption of blockchain in IoT:challenges and solutions

Conventional Internet of Things(IoT)ecosystems involve data streaming from sensors,through Fog devices to a centralized Cloud server.Issues that arise include privacy concerns due to third party management of Cloud servers,single points of failure,a bottleneck in data flows and difficulties in regularly updating firmware for millions of smart devices from a point of security and maintenance perspective.Blockchain technologies avoid trusted third parties and safeguard against a single point of failure and other issues.This has inspired researchers to investigate blockchain’s adoption into IoT ecosystem.In this paper,recent state-of-the-arts advances in blockchain for IoT,blockchain for Cloud IoT and blockchain for Fog IoT in the context of eHealth,smart cities,intelligent transport and other applications are analyzed.Obstacles,research gaps and potential solutions are also presented.