An automatic health monitoring platform using deep machine learning and artificial intelligence (AI) based on Zigbee wireless sensors
Nahom G Ghebremeskel, Dr. Vahid Emamian
The goal of this research is to develop a human health monitoring platform using deep machine learning and artificial intelligence (AI). The signal collected from human body will be transmitted over a wireless channel to the platform using a Zigbee sensors. During this experimental research, we collect and process real time ECG signals to detect and alert regarding health issues. Several human body signals such as heartbeat, blood pressure, brain activities, and body temperature can be collected using various sensors, however, in this research our focus will be on Electrocardiogram (ECG) signals. ECG signal are collected and transmitted through Zigbee transceiver to the AI & deep learning platform where the signal is processed for diagnosis of heart issues. ZigBee transceivers can acquire and transmit/receive signals over a wireless channel. They offer efficient relay protocol, good transmission range, and flexible network structure with emphasis or power consumption efficiency.
Processing the ECG signals in real time and continuedly will help us detect heart issues at the early stage. We will use an AI and deep machine learning platform to train the machine detect and/or predict early stages of heart disease by real-time and continues processing of the ECG signals.
keywords: Electrocardiogram (ECG), Arrhythmia, Convolutional Neural Network (CNN), Data Augmentation, Xbee, AD 8232, Zigbee Communication, Heart Disease Detection.
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