KEdge: Fuzzy-Based Multi-AI Model Coalescence Solution for Mobile Healthcare System

Abstract

In this work, we propose a condition-aware analytical framework—KEdge—for health condition recommendation in Internet of Things (IoT) based mobile healthcare systems. Procuring data from multiple sensors and making a singular assessment from them is a challenging task. KEdge overcomes such an issue by determining the severity of the patient by determining a condition index (CI) using a two-step analytical framework and a multiple rule fuzzy inference system (FIS). In the first step, KEdge detects the heart severity condition using a convolutional neural network model and, in the second step, it detects the respiratory condition using a random forest classification model. KEdge also utilizes auscultation sounds from SkopEdge (a digital stethoscope) for assessing the heartbeats. Through extensive experiments, we observe that KEdge identifies the arrhythmia condition with an accuracy of 98.53% and respiratory condition by 98.68%. KEdge considers the analytical predictions and analysis from SkopEdge to evaluate the CI for recommending the overall health condition using Mamdani FIS. We observe that KEdge is suitable for resource-constrained IoT devices providing memory consumption of 6.6%. On offloading the same to the fog nodes, we observe improved CPU utilization with data upload rates in the order of 25 kb/s (KEdge) and 5 Mb/s (SkopEdge).