Secured Smart Healthcare Monitoring System Based on Iot

Technology plays the major role in healthcare not only for sensory devices but also in communication, recording and display device. It is very important to monitor various medical parameters and post operational days. Hence the latest trend in Healthcare communication method using IOT is adapted. Internet of things serves as a catalyst for the healthcare and plays prominent role in wide range of healthcare applications. In this project the PIC18F46K22 microcontroller is used as a gateway to communicate to the various sensors such as temperature sensor and pulse oximeter sensor. The microcontroller picks up the sensor data and sends it to the network through Wi-Fi and hence provides real time monitoring of the health care parameters for doctors. The data can be accessed anytime by the doctor. The controller is also connected with buzzer to alert the caretaker about variation in sensor output. But the major issue in remote patient monitoring system is that the data as to be securely transmitted to the destination end and provision is made to allow only authorized user to access the data. The security issue is been addressed by transmitting the data through the password protected Wi-Fi module ESP8266 which will be encrypted by standard AES128 and the users/doctor can access the data by logging to the html webpage. At the time of extremity situation alert message is sent to the doctor through GSM module connected to the controller. Hence quick provisional medication can be easily done by this system. This system is efficient with low power consumption capability, easy setup, high performance and time to time response. DOI: 10.17762/ijritcc2321-8169.150712

IoT And Cloud Server Based Wearable Health Sensors Monitoring System

The healthcare monitoring systems has emerged as one of the most vital system and became technology oriented from the past decade. Humans are facing a problem of unexpected death due to various illness which is because of lack of medical care to the patients at right time. The primary goal was to develop a reliable patient monitoring system using IoT so that the healthcare professionals can monitor their patients, who are either hospitalized or at home using an IoT based integrated healthcare system with the view of ensuring patients are cared for better. A mobile device based wireless healthcare monitoring system was developed which can provide real time online information about physiological conditions of a patient mainly consists of sensors, the data acquisition unit, microcontroller (i.e., Arduino), and programmed with a software (i.e., JAVA). The patient’s temperature, heart beat rate, EEG data are monitored, displayed and stored by the system and sent to the doctor’s mobile containing the application. Thus IOT based monitoring system effectively monitor patient’s health status and save life on time

IoT Based Patient’s Smart Healthcare Monitoring and Recording Using GSM Module

An IoT based smart healthcare monitoring system is presented in this study. A local database, sensors, a microcontroller, and a GSM module make up the suggested system. Here, we’re using an Arduino Uno to attach sensors, which then sense the environment and record data that is sent over the internet using the GSM protocol. The health of patients in isolated or rural locations without access to medical services can be tracked using this project. The importance of offering patients who were anticipated to decrease travel and direct contact with others remote patient monitoring services became even more clear when the epidemic struck. When there is an abnormal activity identified, healthcare team will get a message and they would make a call and prescribe the activities which need to be do for emergency and even it doesn’t work ambulance is immediately depart to that particular patient’s location since we have the patient’s personal info about his location and his/her relative emergency mobile number

A Internet of Things Improvng Deep Neural Network Based Particle Swarm Optimization Computation Prediction Approach for Healthcare System

Internet of Things (IoT) systems tend to generate with energy and good data to process and responding. In internet of things devices, the most important challenge when sending data to the cloud the level of energy consumption. This paper introduces an energy-efficient abstraction method data collection in medical with IoT-based for the exchange. Initially, the data required for IoT devices is collected from the person. First, Adaptive Optimized Sensor-Lamella Zive Welch (AOSLZW) is a pressure sensing prior to the data transmission technique used in the process. A cloud server is used data reducing  the amount of data sent from IoT devices to the AOSLZW strategy. Finally, a deep neural network (DNN) based on Particle Swarm Optimization (PSO) known as DNN-PSO algorithm is used for data sensed result model make decisions based as a predictive to make it. The results are studied under distinct scenarios of the presented of the performance for AOSLZW-DNN-PSO method, for that simation are studied under different sections. This current pattern of simalation results indicates that the AOSLZW-DNN-PSO method is effective under several aspects

An Intelligent Decision Support Ensemble Voting Model for Coronary Artery Disease Prediction in Smart Healthcare Monitoring Environments

Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low- and middle-income countries. The preferred and most precise diagnostic tool for CAD is angiography, but it is invasive, expensive, and technically demanding. However, the research community is increasingly interested in the computer-aided diagnosis of CAD via the utilization of machine learning (ML) methods. The purpose of this work is to present an e-diagnosis tool based on ML algorithms that can be used in a smart healthcare monitoring system. We applied the most accurate machine learning methods that have shown superior results in the literature to different medical datasets such as RandomForest, XGboost, MLP, J48, AdaBoost, NaiveBayes, LogitBoost, KNN. Every single classifier can be efficient on a different dataset. Thus, an ensemble model using majority voting was designed to take advantage of the well-performed single classifiers, Ensemble learning aims to combine the forecasts of multiple individual classifiers to achieve higher performance than individual classifiers in terms of precision, specificity, sensitivity, and accuracy; furthermore, we have benchmarked our proposed model with the most efficient and well-known ensemble models, such as Bagging, Stacking methods based on the cross-validation technique, The experimental results confirm that the ensemble majority voting approach based on the top 3 classifiers: MultilayerPerceptron, RandomForest, and AdaBoost, achieves the highest accuracy of 88,12% and outperforms all other classifiers. This study demonstrates that the majority voting ensemble approach proposed above is the most accurate machine learning classification approach for the prediction and detection of coronary artery disease.Comment: International Journal of Advanced Computer Science and Applications 202

Exploring IoT in Smart Cities: Practices, Challenges and Way Forward

The rise of Internet of things (IoT) technology has revolutionized urban living, offering immense potential for smart cities in which smart home, smart infrastructure, and smart industry are essential aspects that contribute to the development of intelligent urban ecosystems. The integration of smart home technology raises concerns regarding data privacy and security, while smart infrastructure implementation demands robust networking and interoperability solutions. Simultaneously, deploying IoT in industrial settings faces challenges related to scalability, standardization, and data management. This research paper offers a systematic literature review of published research in the field of IoT in smart cities including 55 relevant primary studies that have been published in reputable journals and conferences. This extensive literature review explores and evaluates various aspects of smart home, smart infrastructure, and smart industry and the challenges like security and privacy, smart sensors, interoperability and standardization. We provide a unified perspective, as we seek to enhance the efficiency and effectiveness of smart cities while overcoming security concerns. It then explores their potential for collective integration and impact on the development of smart cities. Furthermore, this study addresses the challenges associated with each component individually and explores their combined impact on enhancing urban efficiency and sustainability. Through a comprehensive analysis of security concerns, this research successfully integrates these IoT components in a unified approach, presenting a holistic framework for building smart cities of the future. Integrating smart home, smart infrastructure, and smart industry, this research highlights the significance of an integrated approach in developing smart cities

Predictive Internet of Things Based Detection Model of Comatose Patient using Deep Learning

The needs and demands of the healthcare sector are increasing exponentially. Also, there has been a rapid development in diverse technologies in totality. Hence varied advancements in different technologies like Internet of Things (IoT) and Deep Learning are being utilised and play a vital role in healthcare sector. In health care domain, specifically, there is also increasing need to find the possibility of patient going into coma. This is because if it is found that the patient is going into coma, preventive steps could be initiated helping patient and this could possibly save the life of the patient. The proposed work in this paper is in this direction whereby the advancement in technology is utilised to build a predictive model towards forecasting the chances of a patient going into coma state. The proposed system initially consists of different medical devices like sensors which take inputs from the patient and helps aid to monitor the condition of the patient. The proposed system consists of varied sensing devices which will help to record patient’s details such as blood pressure (B.P.), pulse rate, heart rate, brain signal and continuous monitoring the motion of coma patient. The various vital parameters from the patient are taken in continuously and displayed across a graphical display unit. Further as and when even if one vital parameter exceeds certain thresholds, the probability that patient will go into coma increases. Immediately an alert is given in. Further, all such records where there are chances that patient goes into coma state are stored in cloud. Subsequently, based on the data retrieved from the cloud a predictive model using Convolutional Neural Network (CNN) is built to forecast the status of the coma patient as an output for any set of health-related parameters of the patient. The effectiveness of the built predictive model is evaluated in terms of performance metrics such as accuracy, precision and recall. The built forecasting model displays high accuracy up to 98%. Such a system will greatly benefit health sector and coma patients and enable build futuristic and superior predictive and preventive model helping in reducing cases of patient going into coma state

A Wireless ECG Device with Mobile Applications for Android

Electrocardiograph (ECG) is a measuring device that used in hospital to monitor electrical activity of heart. Commonly used ECG device is a Holter monitor, a portable and wired device, which is bulky and not suitable for measuring and recording athlete's heart activity during training. The objective of this study was to design the ECG monitoring system as an Internet of Things (IoT) device, equipped with a temperature detector to detect user's body temperature. The ECG signals and the temperature were transmitted wirelessly using Bluetooth transmission to the mobile applications (apps). Both signals were set to display on mobile apps which was developed using Blynk application. At the end of this project, the signals were shown on the mobile apps and the user could monitor their own ECG signals as well as to share with their caretaker or physician later

Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%)