Design and Implementation of an IoT Based Smart Digestive Health Monitoring Device for Identification of Digestive Conditions

Abstract

Over the past few decades, there has been a significant rise in wearable healthcare technologies that have been playing a major role all over the world in monitoring health, alerting individuals during deviations from their normal health conditions and assisting them to stay fit and healthy. Due to the modern lifestyle and consumption of unhealthy food products, there has been an adverse effect on digestive health standards. In this work, a wearable device with textile electrodes is designed and developed to analyze the digestive conditions, namely, pre-prandial and post-prandial, using Electrogastrogram (EGG) signals. Further, the proposed device is comprised of textile electrodes as a sensor, an Analog-to-Digital Converter (ADC) with a Programmable Gain Amplifier (PGA), a Microcontroller with an inbuilt WirelessFidelity (WiFi) module, and an Internet of Things (IoT) cloud platform. Also, the EGG signals are acquired under two different conditions, namely, pre-prandial and post-prandial conditions, and then the Long Short Term Memory (LSTM) deep learning model is utilized to classify pre-prandial and post-prandial EGG signals to identify the eating habits of normal individuals. Results demonstrate that the proposed approach is capable of classifying the pre-prandial and post-prandial EGG signals, which, in turn, identify the fasting or ingestion state of normal individuals.

Keywords

• bio-signal acquisition
• electrogastrograms
• Fast Fourier Transform
• Internet of Things
• pre-prandial and post-prandial conditions