scholarly journals IoT based Heart Attack Detection, Heart Rate and Temperature Monitor

2017 ◽  
Vol 170 (5) ◽  
pp. 26-30 ◽  
Author(s):  
Gowrishankar S. ◽  
Prachita M. ◽  
Arvind Prakash
2020 ◽  
Author(s):  
Suchitra Giri ◽  
Ujjwal Kumar ◽  
Varsha Sharma ◽  
Satish Kumar ◽  
Sikha Kumari ◽  
...  

2019 ◽  
Vol 1 (6) ◽  
pp. 61-70
Author(s):  
Vaishnave A.K ◽  
Jenisha S.T ◽  
Tamil Selvi S

The Internet of Things (IoT) is inter communication of embedded devices using networking technologies. The IoT will be one of the important trends in future; can affect the networking, business and communication. In this paper, proposing a remote sensing parameter of the human body which consists of pulse and temperature. The parameters that are used for sensing and monitoring will send the data through wireless sensors. Adding a web based observing helps to keep track of the regular status of patient. The sensing data will be continuously collected in a database and will be used to inform patient to any unseen problems to undergo possible diagnosis. Experimental results prove the proposed system is user friendly, reliable, economical. IoT typically expected to propose the advanced high bandwidth connectivity of embedded devices, systems and services which goes beyond machine –to – machine (M2M) context. The advanced connectivity of devices aide in automation is possible in nearly all field. Everyone today is so busy in their lives; even they forget to take care of their health. By keeping all these things in minds, technology really proves to be an asset for an individual. With the advancement in technology, lots of smart or medical sensors came into existence that continuously analyzes individual patient activity and automatically predicts a heart attack before the patient feels sick.


2003 ◽  
Vol 92 (2) ◽  
pp. 234-236 ◽  
Author(s):  
Ahmad Sajadieh ◽  
Verner Rasmussen ◽  
Hans Ole Hein ◽  
Jørgen Fischer Hansen

2021 ◽  
Vol 11 (1) ◽  
pp. 7-19
Author(s):  
Ibrahima Bah

Machine Learning, a branch of artificial intelligence, has become more accurate than human medical professionals in predicting the incidence of heart attack or death in patients at risk of coronary artery disease. In this paper, we attempt to employ Artificial Intelligence (AI) to predict heart attack. For this purpose, we employed the popular classification technique named the K-Nearest Neighbor (KNN) algorithm to predict the probability of having the Heart Attack (HA). The dataset used is the cardiovascular dataset available publicly on Kaggle, knowing that someone suffering from cardiovascular disease is likely to succumb to a heart attack. In this work, the research was conducted using two approaches. We use the KNN classifier for the first time, aided by using a correlation matrix to select the best features manually and faster computation, and then optimize the parameters with the K-fold cross-validation technique. This improvement led us to have an accuracy of 72.37% on the test set.


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

Author(s):  
Isna Fatimatuz Zahra ◽  
I Dewa Gede Hari Wisana ◽  
Priyambada Cahya Nugraha ◽  
Hayder J Hassaballah

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.


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