An IoT framework for Bio-medical sensor data acquisition and machine learning for early detection

Internet of things in clinical domain has opened up new possibilities in remote monitoring of patients by connecting healthcare bio-sensor systems over the internet. This paper has proposed a working prototype of a real-time health monitoring system, which collects sensor data from body area network and communicates the data to a predictive model that is trained on historical clinical data. The prototype is equipped with Analog DeviceTM AD 8232 module for electrocardiogram and heart rate monitoring. CYPRESS CY8CKIT-042-BLE-A PSoC® 4 Bluetooth® Low Energy Pioneer Kit is used for implementation of a body area network, which collects patient's vitals and communicates the sensor data to a Raspberry Pi3. The gateway device between WPAN (Bluetooth® Low Energy) and WLAN (IEEE 802.11n) is implemented using Raspberry Pi3. The gateway device collects the sensor data over a Bluetooth personal area network coming from all the connected devices and the data is acquired over internet server. ECG- ST wave and heart rate data are sent to the cloud server from the sensors. On the server, a machine learning model is deployed to predict any malfunctions based on sensor readings posted from the real-time health monitoring system and generate early alerts. We have obtained >90% prediction accuracy with random forest classifier using the UCI heart diseases repository.