Heartbeat and Body Temperature Monitoring System Based on Artificial Neural Networks - Tan Suryani Sollu; Alamsyah Alamsyah; Eko Setijadi (Jurusan Teknik Elektro, Universitas Bangka Belitung, 2022)
Heartbeat and body temperature are vital sign parameters for paramedics in strengthening the diagnosis of a disease. Medical staff generally use an electrocardiogram and thermometer to check the heart rate and body temperature. These tools are still manual and require concentration to get accurate values. This examination system is less useful because it requires a long time to collect data, increasing the burden on medical personnel and rising operational costs. To improve health services optimally, the authors propose the manufacture of heart rate and body temperature monitoring devices for the elderly based on wireless using the Artificial Neural Network (ANN) method. The proposed method can assist medical personnel in diagnosing heart attacks with three conditions (normal, low risk, and high risk). This study aims to assist medical staff in monitoring patients 'health conditions and diagnosing patients' heart disease in real time. This system uses PPG HRM-2511E sensor to detect heart rate and a DS18B20 sensor to detect body temperature. The data detection process uses a raspberry pi, and the decision-making system uses the ANN method. The results of testing the success rate of detecting the heartbeat of 97.90%, and the body temperature of 99.51%. The heart rate and body temperature data processing using ANN went as expected.