scholarly journals Design a Monitoring Device for Heart-Attack Early Detection Based on Respiration Rate and Body Temperature Parameters

2021 ◽  
Vol 3 (3) ◽  
pp. 114-120
Author(s):  
Isna Fatimatuz Zahra ◽  
I Dewa Gede Hari Wisana ◽  
Priyambada Cahya Nugraha ◽  
Hayder J Hassaballah
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Respiration Rate ◽  
Heart Attack ◽  
Detection System ◽  
Warning System ◽  
Piezoelectric Sensor ◽  
Attack Detection ◽  
Sensor Data ◽  
Frequency Module

Acute myocardial infarction, commonly referred to as a heart attack, is the most common cause of sudden death where a monitoring tool is needed that is equipped with a system that can notify doctors to take immediate action. The purpose of this study was to design a heart attack detection device through indicators of vital human signs. The contribution of this research is that the system works in real-time, has more parameters, uses wireless, and is equipped with a system to detect indications of a heart attack. In order for wireless monitoring to be carried out in real-time and supported by a detection system, this design uses a radio frequency module as data transmission and uses a warning system that is used for detection. Respiration rate was measured using the piezoelectric sensor, and body temperature was measured using the DS18B20 temperature sensor. Processing of sensor data is done with ESP32, which is displayed wirelessly by the HC-12 module on the PC. If an indication of a heart attack is detected in the parameter value, the tool will activate a notification on the PC. In every indication of a heart attack, it was found that this design can provide notification properly. The results showed that the largest respiratory error value was 4%, and the largest body temperature error value was 0.55%. The results of this study can be implemented in patients who have been diagnosed with heart attack disease so that it can facilitate monitoring the patient's condition.

scholarly journals Design of Early Warning System Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 66
Author(s):  
Gan Bo ◽  
Jin Shan
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sensor Data ◽  
Landslide Monitoring ◽  
Remote Transmission ◽  
Sensor Data Acquisition

In order to solve the shortcomings of the landslide monitoring technology method, a set of landslides monitoring and early warning system is designed. It can achieve real-time sensor data acquisition, remote transmission and query display. In addition, aiming at the harsh environment of landslide monitoring and the performance requirements of the monitoring system, an improved minimum hop routing protocol is proposed. It can reduce network energy consumption, enhance network robustness, and improve node layout and networking flexibility. In order to realize the remote transmission of data, GPRS wireless communication is used to transmit monitoring data. Combined with remote monitoring center, real-time data display, query, preservation and landslide warning and prediction are realized. The results show that the sensor data acquisition system is accurate, the system is stable, and the node network is flexible. Therefore, the monitoring system has a good use value.

CARRADS: Cross layer based adaptive real-time routing attack detection system for MANETS

2010 ◽  
Vol 54 (7) ◽  
pp. 1126-1141 ◽  
Author(s):  
John Felix Charles Joseph ◽  
Amitabha Das ◽  
Bu-Sung Lee ◽  
Boon-Chong Seet
Keyword(s):  
Real Time ◽  
Detection System ◽  
Attack Detection ◽  
Cross Layer ◽  
Routing Attack

scholarly journals Heart Attack Detection System

IJARCCE ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 204-206
Author(s):  
Abhimanyu. H ◽  
Biju Balakrishnan
Keyword(s):  
Heart Attack ◽  
Detection System ◽  
Attack Detection

scholarly journals Central Monitor Based on Personal Computer Design with SpO2 and Body Temperature Parameters Using Wireless Xbee Pro

2021 ◽  
Vol 3 (1) ◽  
pp. 35-43
Author(s):  
I KOMANG YOGI MAHARDIKA ◽  
Bambang Guruh Irianto ◽  
Torib Hamzah ◽  
Shubhrojit Misra
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Personal Computer ◽  
Health Workers ◽  
Sensor Data ◽  
Computer Design ◽  
Wireless System ◽  
Average Value ◽  
Patient Monitoring System ◽  
Multiple Patients

Central patient monitor that is not real-time and continues will cause inaccuracies monitoring results and also sending data that is still using cable will cause limited distance. The purpose of this research is to design a central monitoring based personal computer via Xbee Pro. The contribution of this research is,  the system works in real-time and continues, more parameters, using wireless, longer transmission distances. So that monitoring can be done in real-time and continue via wireless with more distance, then the wireless system uses the Xbee Pro module which has larger output power and uses the same number of wireless modules between transmitter and receiver. Body temperature was measured using the LM35 sensor and oxygen saturation in the blood was measured using the MAX30100 sensor. Data is sent using Xbee Pro and displayed on a personal computer. At the distance of receiving data approximately 25 meters with a wall divider, obtained results of smooth monitoring without any loss of data. The results showed that the average SpO2 error value was 0.34% in module 1 and 0.68% in module 2. The average value of body temperature error was 0.46% in module 1 and 0.72% in module 2. The results of this research can be implemented in a centralized patient monitoring system at the hospital, making it easier for health workers to monitor multiple patients, with the results of monitoring in real-time and continue, more parameters, via wireless with greater distance.

A sensor based intelligent system for classification and assistance of diabetes patients in telemedicine technology

2020 ◽  
pp. 1-10
Author(s):  
S, Poonguzhali ◽  
Rekha Chakravarthi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Glucose Level ◽  
Heart Attack ◽  
Intelligent System ◽  
Disease Diagnosis ◽  
Wearable Device ◽  
Sensor Data ◽  
Back Propagation Algorithm ◽  
Telemedicine Technology

Diabetes is one of the chronic metabolic disorder. Under diabetic condition, blood glucose level should be properly maintained in order to avoid various major diseases. The condition will be worse when it is not controlled at an earlier stage. Even massive heart attack cannot be identified when the patient has been affected by diabetes. Early diagnosis is required for preventing fatal diseases like cardiac problem, asthma, heart attack etc. In the proposed system measurement of glucose level and Prediction/ diagnosis of diabetes is based on the real time low complexity neural network implemented on a wearable device. A larger network is required for the diagnosis which needs to be present far-off in cloud and initiated for diagnosis and classification process of diabetes whenever it is essential. People can be able to manage and monitor the required basic parameters like heart rate, glucose level, lung condition, pressure of blood using the corresponding light weight biosensors in the wearable device designed through telemedicine technology. The quality of the disease diagnosis and Prediction is improved in this way. Using neural network feed forward prediction model in conjugation with back propagation algorithm and given training data, the system predicts whether the patient is prone to diabetes or not. The proposed work was evaluated using physic sensor data from physio net data base and also tested for real time functioning. The Proposed system found to be efficient in accuracy, sensitivity and fast operative.

Real-Time Position Falsification Attack Detection System for Internet of Vehicles

2021 ◽  
Author(s):  
Goodness Oluchi Anyanwu ◽  
Cosmas Ifeanyi Nwakanma ◽  
Jae-Min Lee ◽  
Dong-Seong Kim
Keyword(s):  
Real Time ◽  
Detection System ◽  
Attack Detection ◽  
Internet Of Vehicles ◽  
Time Position

scholarly journals The Heart Attack Detection by ESP8266 Data Communication at a Real Time to Avoid Sudden Death

2019 ◽  
Vol 1235 ◽  
pp. 012044
Author(s):  
Poltak Sihombing ◽  
Mangasa Manullang ◽  
Dahlan Sitompul ◽  
Imelda Sri Dumayanti
Keyword(s):  
Sudden Death ◽  
Real Time ◽  
Heart Attack ◽  
Data Communication ◽  
Attack Detection

ContainerGuard: A Real-Time Attack Detection System in Container-based Big Data Platform

2020 ◽  
pp. 1-1
Author(s):  
Yulong Wang ◽  
Qixu Wang ◽  
Xingshu Chen ◽  
Dajiang Chen ◽  
Xiaojie Fang ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Detection System ◽  
Attack Detection ◽  
Data Platform

scholarly journals Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2780 ◽  
Author(s):  
Muhammad E. H. Chowdhury ◽  
Khawla Alzoubi ◽  
Amith Khandakar ◽  
Ridab Khallifa ◽  
Rayaan Abouhasera ◽  
...  
Keyword(s):  
Real Time ◽  
Heart Attack ◽  
The United States ◽  
Attack Detection ◽  
The Other ◽  
Support Vector ◽  
Linear Classification ◽  
Road Accidents ◽  
Time Frequency ◽  
St Elevation

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone—that is one in every four deaths—but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time–frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the world.

Sign in / Sign up

Export Citation Format

Share Document