Abstract
Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.
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